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Paper Citation Record · LEDGER

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks

As of 16 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 3 inbound Pith citation observations for arXiv:2507.23172.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.23172 v2

Coverage vector

measured 75 of 75 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:03:32.220111Z

measured 78 of 78 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:47:59.248127Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T11:46:55.739072Z

Reference resolution

75 of 75 outbound references displayed

  • verified exact3
  • verified fuzzy25
  • unresolved47
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e0259aa2-b285-4ab6-9435-193e1a95d66b · outbound

This paper cites Deep reinforcement learning at the edge of the statistical precipice.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Deep reinforcement learning at the edge of the statistical precipice

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:21.547614Z digest=sha256:2cda61ceb57079ea0e1b2b4c9ed717c116d3c25723d057a02041b7c923d9ef74

Observation a5fa4944-397e-44e1-8606-27211b6b0bf6 · outbound

This paper cites Locomujoco: A comprehensive imitation learning benchmark for locomotion.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Locomujoco: A comprehensive imitation learning benchmark for locomotion

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-06T11:03:38.932352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:21.603099Z digest=sha256:86a316b7609d919e72008e6e8d0050403cb632e251f9cc7170523f71b33c945b

Observation 4bad7e69-22dd-4008-acfc-f4649fc76bef · outbound

This paper cites Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger

Reference 3

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local_arxiv, observed 2026-08-06T11:03:33.564907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:21.702708Z digest=sha256:e4cf28a75f360ae3f8ab380cbb47d14394d885904b6095e2afd313d9bb82bb3d

Observation 3827d9ad-3158-4b90-8100-960951aa70db · outbound

This paper cites Layer Normalization.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Layer Normalization

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:21.846873Z digest=sha256:836517ce726139eb66c1ad72877c5c250f3c1fb1e8475cf60eefef407a2395af

Observation 99b10ff1-ae20-4779-bf81-9719f4b0d411 · outbound

This paper cites Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU

Reference 5

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source=arxiv_source observed=2026-08-06T11:03:21.926066Z digest=sha256:3673f2bc5bf3e87e7e98c99aebd8ac19db611877cde13da98df249fb8a5c2c97

Observation 148d73d9-d505-4656-9469-ff55e32f826b · outbound

This paper cites Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX

Reference 6

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no resolver link, observed 2026-08-06T11:03:22.084295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:22.084295Z digest=sha256:1510633d0d4a609f16d7613a1dd65b5ee13e7af5e29428a34cabb6ea7bb7c5cc

Observation 491e358b-41aa-4f9b-9660-e1946ef5fc3b · outbound

This paper cites DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics

Reference 7

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source=arxiv_source observed=2026-08-06T11:03:22.248755Z digest=sha256:d8be4a343b2f7f08cef0ca8d5a1eba4be937c137901002e5738101190c224f7b

Observation 312e53a3-d87f-4fef-81a8-051d536222d3 · outbound

This paper cites Extreme parkour with legged robots.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Extreme parkour with legged robots

Reference 8

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raw_fallback, observed 2026-08-06T11:03:38.787178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:22.450346Z digest=sha256:4a2b1843b06e94283a62205b599059546d13d09e2237d53196d75ba0d376d33c

Observation 35b71f9c-0e24-4310-bd0f-c3675fd4cd26 · outbound

This paper cites Leveraging procedural generation to benchmark reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Leveraging procedural generation to benchmark reinforcement learning

Reference 9

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raw_fallback, observed 2026-08-06T11:03:38.564219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:22.525109Z digest=sha256:a9af6adf2576125be9b7e17cc3eda67e555dc91c581cb7762c998c36923337d8

Observation faac1cd1-27bd-4bce-b510-9776a76ae743 · outbound

This paper cites Sample-efficient reinforcement learning by breaking the replay ratio barrier.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Sample-efficient reinforcement learning by breaking the replay ratio barrier

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:22.601095Z digest=sha256:2c29eed250eec7ac607b425e6f357789d7fbeb09a65b890f8574ad7a07109a1b

Observation bfddbf17-7840-428c-8808-b0106f042cda · outbound

This paper cites Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures

Reference 11

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source=arxiv_source observed=2026-08-06T11:03:22.724083Z digest=sha256:9eaf99309806d66e1bd8bea552ec2a7534e3f581778db4e910fc619bc740e901

Observation 3870f6e1-a552-42ea-b335-6bc2d11dee6b · outbound

This paper cites Franka emika panda robot, 2017.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Franka emika panda robot, 2017

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 54764a4d-0c94-40c1-abf4-a62653d42ccc · outbound

This paper cites Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation

Reference 13

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:22.998932Z digest=sha256:c3460f7160f30a0e392c8dac594e0d23efe53e92d6810b77a5d1c9d89f1980ae

Observation ccd60bd3-ae62-47bd-a072-3bd43242188b · outbound

This paper cites Deep whole-body control: learning a unified policy for manipulation and locomotion.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Deep whole-body control: learning a unified policy for manipulation and locomotion

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-06T11:03:37.965262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:23.113116Z digest=sha256:04a3e579b02418dcadf0581fcaf63cb5cd3a7d2461872ad06c9078eebc5333e5

Observation ab8b6e32-c3dd-4343-aa03-25fb4bda2d62 · outbound

This paper cites Simplifying Deep Temporal Difference Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Simplifying Deep Temporal Difference Learning

Reference 15

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source=arxiv_source observed=2026-08-06T11:03:23.236918Z digest=sha256:96d212d6bff7941fceb298d3e3be7b7899e3051e251990fcfe8e1c63fda78877

Observation 8459246d-52e0-4e88-b6a0-342a40ad399c · outbound

This paper cites Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Reference 16

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no resolver link, observed 2026-08-06T11:03:23.401536Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:23.401536Z digest=sha256:ff547e459bff150990c3fc3f1fa531789669fbf547ed228cd7e133fbad687b56

Observation 7a321585-e291-49c4-9d6e-32adc78b4397 · outbound

This paper cites Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts

Reference 17

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source=arxiv_source observed=2026-08-06T11:03:23.515122Z digest=sha256:3e85db57dbe1446e21fdecdf5c884fb4a469aa18f152eabe3da6d61624d0024e

Observation d59c6f04-43fc-48a7-9b93-b6c32e809d66 · outbound

This paper cites Multi-task deep reinforcement learning with popart.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Multi-task deep reinforcement learning with popart

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:23.598474Z digest=sha256:f9896072c67901c7d5589d9d1b649631dfb6fc60471912a593cad6afd92c59ff

Observation a46b7bb0-bc39-44d3-8dbf-881ccef46813 · outbound

This paper cites Distributed Prioritized Experience Replay.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Distributed Prioritized Experience Replay

Reference 19

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source=arxiv_source observed=2026-08-06T11:03:23.659631Z digest=sha256:7650993ab6cebf954f696f38464b271a00cda870bbcb7202dad96f69f5e5959c

Observation 9e9ffe28-7bb2-4d4b-b8a9-1b7cd0c3554b · outbound

This paper cites Learning agile and dynamic motor skills for legged robots.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Learning agile and dynamic motor skills for legged robots

Reference 20

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:23.793351Z digest=sha256:49fb466d7a06ad0343bbaa165dae507a0867d3b4e9c4e69f3893b4aec1f931bb

Observation f3258f4a-b1b9-43b5-b886-798a84a21ba2 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 21

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source=arxiv_source observed=2026-08-06T11:03:23.880190Z digest=sha256:6caeff9a7a7e5e4914d99d3b4ed0ab2bf39418fbf9201b543f8bc884963b7f84

Observation dd30d924-2919-47fd-aa93-5fedc6e7b561 · outbound

This paper cites an unresolved cited work.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Unresolved cited work

Reference 22

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:23.976570Z digest=sha256:52f81e81363f2992893c7e5e8dcbc6e70b7ef4b33e47f2e58ab257e01ceb010b

Observation 5ab0480f-88c2-482e-8a09-ab1a4f53cc69 · outbound

This paper cites A survey of zero-shot generalisation in deep reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks A survey of zero-shot generalisation in deep reinforcement learning

Reference 23

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raw_fallback, observed 2026-08-06T11:03:37.457058Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:24.116467Z digest=sha256:479ce10d07e149cf4602834d4d63d1f33248c30e768325dfcb897bb519e357a4

Observation 4395375f-7a86-46d0-b96e-855ff9522a56 · outbound

This paper cites Pgx: Hardware-accelerated parallel game simulators for reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Pgx: Hardware-accelerated parallel game simulators for reinforcement learning

Reference 24

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:24.267463Z digest=sha256:f7c0a2ef65f3ea23ec4a08b8e45c0a68cb7a212e680331b1b150b98a5de04669

Observation 0e12560f-4329-4fbf-8dd1-372ed53574d4 · outbound

This paper cites gymnax : A JAX -based reinforcement learning environment library, 2022.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks gymnax : A JAX -based reinforcement learning environment library, 2022

Reference 25

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raw_fallback, observed 2026-08-06T11:03:37.121591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:24.397822Z digest=sha256:9f2e4a79c66ac0262273f8c415c0372882c8b483adc3a7610a6b40d4cf9f8cda

Observation 5a40fe96-9794-4111-bade-18bcb46fbbfa · outbound

This paper cites Learning quadrupedal locomotion over challenging terrain.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Learning quadrupedal locomotion over challenging terrain

Reference 26

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raw_fallback, observed 2026-08-06T11:03:36.887243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:24.559099Z digest=sha256:9334fafb47f35bc8a541d748b7e24dd98b37c5e161dfe6704f2f9490062c6450

Observation ae0ec9ea-4485-46d2-9123-ad50587f3a1b · outbound

This paper cites Parallel q -learning: Scaling off-policy reinforcement learning under massively parallel simulation.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Parallel q -learning: Scaling off-policy reinforcement learning under massively parallel simulation

Reference 27

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raw_fallback, observed 2026-08-06T11:03:36.744012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:24.774942Z digest=sha256:5de3dc32a5acbe11e7f484b2d42e3a7564b9fe391d7e68ce96f603e1d5448cc0

Observation 364232bc-313b-4d15-8146-02d236d6c375 · outbound

This paper cites Rllib: Abstractions for distributed reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Rllib: Abstractions for distributed reinforcement learning

Reference 28

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raw_fallback, observed 2026-08-06T11:03:36.568419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:24.850498Z digest=sha256:97991e4640288f2c5aabf2e5ae06675a0e27bd640ce0a3d75798dba183fbba3d

Observation 3ab1ae18-ad23-4ec2-a8df-b474ab50a07c · outbound

This paper cites Gpu-accelerated robotic simulation for distributed reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Gpu-accelerated robotic simulation for distributed reinforcement learning

Reference 29

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raw_fallback, observed 2026-08-06T11:03:36.382119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:25.062255Z digest=sha256:3662c4fdf9a55f876cf7d07c9b42ce0ba91ed624f038d344d81cefe9c41982fc

Observation d37a556b-2458-43da-919f-b8703e104bfc · outbound

This paper cites Eurekaverse: Environment Curriculum Generation via Large Language Models.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 30

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:25.196049Z digest=sha256:0f29dd28184d1360ca18fce78967b334b1fecead00a037193597e91166141c3a

Observation 47eec33a-a586-4242-80be-6a08a54dd6b5 · outbound

This paper cites FAMO: Fast Adaptive Multitask Optimization.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks FAMO: Fast Adaptive Multitask Optimization

Reference 31

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source=arxiv_source observed=2026-08-06T11:03:25.316396Z digest=sha256:c18afebff0fd726a77eb96e4382bc411760f98de279023048816ef634504f6de

Observation 089e5355-0b7b-4230-b443-348793f2127b · outbound

This paper cites LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

Reference 32

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:25.514380Z digest=sha256:2a2f318e47274271ad74423bc39737f6bbe2fc6142b0f5e62300cd490456ffef

Observation 9dcc7e5e-a8a9-45e2-b9b0-eefce8af99a2 · outbound

This paper cites Conflict-Averse Gradient Descent for Multi-task Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Conflict-Averse Gradient Descent for Multi-task Learning

Reference 33

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:25.650538Z digest=sha256:ced89f1d439e3d506322648fa295462e7247b8204d75352c346fc03c0dae4a56

Observation 6e5a73cd-437f-4547-aef0-4bc354ce73fd · outbound

This paper cites Perpetual humanoid control for real-time simulated avatars.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Perpetual humanoid control for real-time simulated avatars

Reference 34

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raw_fallback, observed 2026-08-06T11:03:36.210790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:25.817066Z digest=sha256:3bd7c59d27eaabe5b9196b674eb47f444d8157d78ca605d7cad4a725f13a6922

Observation 54044885-8e42-4ff7-82e6-3826c246efd0 · outbound

This paper cites rl-games: A high-performance framework for reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks rl-games: A high-performance framework for reinforcement learning

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-06T11:03:36.036181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:26.017917Z digest=sha256:82c0ace1942437b0c36a92e5dd684438888f63c148b16f8cb1c1194531b5804c

Observation ce8db6ca-13f9-4303-8011-c66f60ef4308 · outbound

This paper cites Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

Reference 36

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source=arxiv_source observed=2026-08-06T11:03:26.184138Z digest=sha256:5a6b68a30f97ecc9a4ecf295ce6994368127d0477cd86bcbfde8eb889c5b9a78

Observation 51f0bb4e-13d4-4c88-b0d5-0942b92d19ff · outbound

This paper cites Rapid locomotion via reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Rapid locomotion via reinforcement learning

Reference 37

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source=arxiv_source observed=2026-08-06T11:03:26.290341Z digest=sha256:25e3a01cc056f0d16d23245b4b69c16797e3b15378cd7ddfcfdc5cbe56ced7a4

Observation d54f2052-a4f1-4f94-88de-cc7103c26e2b · outbound

This paper cites Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning

Reference 38

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source=arxiv_source observed=2026-08-06T11:03:26.459024Z digest=sha256:da927f722d0b8a8adc8ebe27897b3a6e72625344b17bdafa0a79338a229d4050

Observation bb8220b2-4ab6-4d99-bfc5-e802104815c9 · outbound

This paper cites Orbit: A unified simulation framework for interactive robot learning environments.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Orbit: A unified simulation framework for interactive robot learning environments

Reference 39

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source=arxiv_source observed=2026-08-06T11:03:26.674694Z digest=sha256:bac35558cc69179f5078cb72860bd089dae3544b58ee66b9689cc6bc5b52a74d

Observation 549915d5-7361-4756-a42a-b11ed3a2739d · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Playing Atari with Deep Reinforcement Learning

Reference 40

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source=arxiv_source observed=2026-08-06T11:03:26.825320Z digest=sha256:bfbf09fe1eddb14318e3518b856f03fabf72ddc332c6a5791d7ba06bed389f9b

Observation 657a0540-9a52-41b7-9faf-4514d3bfd4bb · outbound

This paper cites Rusu, Joel Veness, Marc G.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Rusu, Joel Veness, Marc G

Reference 41

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verified fuzzy
raw_fallback, observed 2026-08-06T11:03:35.834992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:26.959536Z digest=sha256:50c3fd17f03d78692c01fc27595da15d8c43ac46711e4411b649ced83a40de75

Observation 042c9cc2-100a-4ca5-a884-dab096552ee5 · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Asynchronous methods for deep reinforcement learning

Reference 42

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no resolver link, observed 2026-08-06T11:03:27.178424Z

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source=arxiv_source observed=2026-08-06T11:03:27.178424Z digest=sha256:06e02400cd32d457f04aeae1ca605e4641c3c832cf5b426ca613bb2dddd4b9a4

Observation 6adb8350-b470-4884-8cee-2a8e9ef67971 · outbound

This paper cites POPGym: Benchmarking Partially Observable Reinforcement Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks POPGym: Benchmarking Partially Observable Reinforcement Learning

Reference 43

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no resolver link, observed 2026-08-06T11:03:27.386150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:27.386150Z digest=sha256:589aa4747edb6637e4a624e0fa569b38d2833a5c193bf5b4952b9bcfa51d0bb2

Observation 60f8519e-7bb0-4d02-a5dd-db22594ca89e · outbound

This paper cites Massively Parallel Methods for Deep Reinforcement Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Massively Parallel Methods for Deep Reinforcement Learning

Reference 44

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source=arxiv_source observed=2026-08-06T11:03:27.536045Z digest=sha256:f488c1944ef0458fdb4ee679fa70324e1d6f06822a8e00b681742a8df00568fd

Observation 21094dcb-5b7d-4cdc-9496-eda1c508bba8 · outbound

This paper cites Learning Dexterous In-Hand Manipulation.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Learning Dexterous In-Hand Manipulation

Reference 45

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:27.683420Z digest=sha256:ef3327265d495dc95514766e933270b607016b90b8edc7c8b5d339bb9daf81f1

Observation 94b606da-e505-4439-a907-8aea010ac98d · outbound

This paper cites OGBench: Benchmarking Offline Goal-Conditioned RL.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks OGBench: Benchmarking Offline Goal-Conditioned RL

Reference 46

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no resolver link, observed 2026-08-06T11:03:27.866008Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:27.866008Z digest=sha256:2281d37b8ee4f03b69b42b4abb1ea273d628aae8158715c0717e215c032b7e1c

Observation 8025aec0-f76e-4f0f-80f3-3c664fa6c409 · outbound

This paper cites Sample factory: Egocentric 3d control from pixels at 100000 fps with asynchronous reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Sample factory: Egocentric 3d control from pixels at 100000 fps with asynchronous reinforcement learning

Reference 47

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verified fuzzy
raw_fallback, observed 2026-08-06T11:03:35.709263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:28.009488Z digest=sha256:22fdf1466aa53c803889eba8c17750221f1194936b1a5941ae6c61ba55d4952e

Observation 56cf0269-8829-47f6-9178-353ac92748be · outbound

This paper cites Learning to Push by Grasping: Using multiple tasks for effective learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Learning to Push by Grasping: Using multiple tasks for effective learning

Reference 48

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verified exact
local_arxiv, observed 2026-08-06T11:03:33.005502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:28.133785Z digest=sha256:343729b3b6947f7246724c6edb8cdd05bb13b3e63fe6c5bf6ae8be338e53b44c

Observation a31f0dba-12cf-4f62-b3ad-670077d431ca · outbound

This paper cites Learning to walk in minutes using massively parallel deep reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Learning to walk in minutes using massively parallel deep reinforcement learning

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-06T11:03:35.534278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:28.332033Z digest=sha256:84ebb9dc40dd64fff3c6e022e7da939e0ca77b053defa17c402cf57dee90fb4c

Observation 40a0afb0-41b0-49d2-8666-7bfd43cfeec4 · outbound

This paper cites JaxMARL: Multi-Agent RL Environments and Algorithms in JAX.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 50

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T11:03:28.485294Z digest=sha256:aeccaf5a9a5a05b570d5ca26657b54c93aa072d96b063ad2df2d0b04a183014d

Observation 842ede67-ce26-4d2e-9dcf-cef77f684586 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Proximal Policy Optimization Algorithms

Reference 51

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source=arxiv_source observed=2026-08-06T11:03:28.602247Z digest=sha256:a7e7e7336f6f1f1fb8c2909549080ededd6c285e0e8c0470e42cb532cebb3d3a

Observation 0ee4ca13-facb-48a3-9191-b09fb5d5866d · outbound

This paper cites Solving Continuous Control via Q-learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Solving Continuous Control via Q-learning

Reference 52

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verified exact
local_arxiv, observed 2026-08-06T11:03:32.734490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:28.770546Z digest=sha256:afab7ddf8508bd4966089b2a327ccacbde4d59ec069f9627fa6050fada393cc6

Observation d968c205-8934-42fd-b2a7-ad78bab7f571 · outbound

This paper cites HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation

Reference 53

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no resolver link, observed 2026-08-06T11:03:28.917713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:28.917713Z digest=sha256:674bfe664aba4886d1e16cccc9c3aec46cd6c3b42b04a489430fc8415f64bf09

Observation 8291e303-13cf-4d50-8383-79c9691d366f · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 54

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no resolver link, observed 2026-08-06T11:03:29.080753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:29.080753Z digest=sha256:8637366f274e92f80c931584dabdcd1cca5b5ac2f1d206d8916d8a31dd719967

Observation 0b2a84a9-6694-411d-a3a9-4ee50ab02404 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Mastering the game of go with deep neural networks and tree search

Reference 55

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no resolver link, observed 2026-08-06T11:03:29.291269Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:29.291269Z digest=sha256:18c82e9954c2ee4126251b068e593d811da568854b50778429079673fda26b85

Observation 8edd2085-984b-4cd6-a003-1f3f81c939a4 · outbound

This paper cites Sapg: Split and aggregate policy gradients.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Sapg: Split and aggregate policy gradients

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:03:35.336689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:29.405988Z digest=sha256:d2fa241b68230e26643fb9eac374c254c4d1f45e5b082aab769215ca5ae3ae81

Observation b44df3ee-a281-468c-8b6e-fd5c22f9e1ed · outbound

This paper cites Mtrl - multi task rl algorithms.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Mtrl - multi task rl algorithms

Reference 57

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verified fuzzy
raw_fallback, observed 2026-08-06T11:03:34.953844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:29.509725Z digest=sha256:8b06260c9c706d287ffd6e57558b024253637bba59b9184fefbc2d010d79b9cf

Observation b98ea7ac-7bc8-4b88-9745-60fcd076016a · outbound

This paper cites Multi-task reinforcement learning with context-based representations.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Multi-task reinforcement learning with context-based representations

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T11:03:34.630161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:29.666591Z digest=sha256:854051cbd011f484ea48256f09cad21d833c5cc22aa7f088e37d71bf7c6646ef

Observation 73c4d6a4-1bee-416e-a3ef-9a44468a2bb7 · outbound

This paper cites PaCo: Parameter-Compositional Multi-Task Reinforcement Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks PaCo: Parameter-Compositional Multi-Task Reinforcement Learning

Reference 59

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no resolver link, observed 2026-08-06T11:03:29.790459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:29.790459Z digest=sha256:9f100def39ab4ced960399fb80885919bcd9fc71379973198d909295ea9a7e3b

Observation 4c905634-aadf-4819-961e-8212124c9f4b · outbound

This paper cites Value-Decomposition Networks For Cooperative Multi-Agent Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Value-Decomposition Networks For Cooperative Multi-Agent Learning

Reference 60

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no resolver link, observed 2026-08-06T11:03:29.901954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:29.901954Z digest=sha256:ef18cfa739f4e4f58429e33fee0b9ed738b308e126d64e6bfd8376f58191413c

Observation 914f3f29-610e-43fc-bde4-5c3aa65780a4 · outbound

This paper cites Policy gradient methods for reinforcement learning with function approximation.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Policy gradient methods for reinforcement learning with function approximation

Reference 61

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no resolver link, observed 2026-08-06T11:03:30.091789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:30.091789Z digest=sha256:7d53c42d4b22b9190638201fd142fef07d0a4db8cb139fd9e4338ab043d4717d

Observation 9ea2fe3f-5da3-46b9-84fa-74f99b29251f · outbound

This paper cites ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI

Reference 62

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no resolver link, observed 2026-08-06T11:03:30.239033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:30.239033Z digest=sha256:0877037d706232ff36cbf83283641f2b8d62a02e977bd7ac5a241b47a33d0fd6

Observation 68c89edc-604f-4cc5-9864-f4518319b735 · outbound

This paper cites DeepMind Control Suite.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks DeepMind Control Suite

Reference 63

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:30.405738Z digest=sha256:9d692f2a612c330c20c4228f37d89ac38aed17ee73b07973a22952568c7a03b4

Observation bcf13415-3bb3-4bda-997f-b352ffbfb748 · outbound

This paper cites Mujoco: A physics engine for model-based control.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Mujoco: A physics engine for model-based control

Reference 64

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:30.591670Z digest=sha256:cfff45f4f7a997fa0907261c3b8eded34d2491b937ba9de246d9fb44071ca662

Observation 2e5a82a9-c46a-48eb-9fff-257bfa6a15c9 · outbound

This paper cites Go1 User Manual.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Go1 User Manual

Reference 65

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verified fuzzy
raw_fallback, observed 2026-08-06T11:03:34.371334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:30.727389Z digest=sha256:e90e2fab44f408c321556c1b5a60482b7438ad8ec8e25262452d536c4814f78e

Observation 79daa933-3cb6-46f4-adc2-430add7b2e7d · outbound

This paper cites Dueling Network Architectures for Deep Reinforcement Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Dueling Network Architectures for Deep Reinforcement Learning

Reference 66

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no resolver link, observed 2026-08-06T11:03:30.851313Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:30.851313Z digest=sha256:a30d9485132540457418b48e41e33f87c172e780cf335061535dc3a986d78917

Observation 76425055-dca4-44c7-be27-c1107a9aa233 · outbound

This paper cites Outracing champion gran turismo drivers with deep reinforcement learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Outracing champion gran turismo drivers with deep reinforcement learning

Reference 67

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verified fuzzy
raw_fallback, observed 2026-08-06T11:03:34.063335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:30.999200Z digest=sha256:ac76b2906927c3fbccc05785c51319841194a93d8b8556aab3c91ec9d86cb41d

Observation e35af45e-1f0b-448d-b13c-8b2845a3e1bb · outbound

This paper cites Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation

Reference 68

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unresolved
no resolver link, observed 2026-08-06T11:03:31.098377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:31.098377Z digest=sha256:273f7129e4ff10fecf12b2971ae620ed1f9ea5bee175dec68518fad22f814205

Observation b81ebc2e-3859-43b7-8cf5-1912b4c15131 · outbound

This paper cites Multi-Task Reinforcement Learning with Soft Modularization.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Multi-Task Reinforcement Learning with Soft Modularization

Reference 69

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no resolver link, observed 2026-08-06T11:03:31.215734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:31.215734Z digest=sha256:007afe9c81b2541415df831bf212916ed0b2ce35bbf48e0bac983b16d0f0d5bd

Observation 6f03fb84-dc42-4b67-b1a5-abfff31214d3 · outbound

This paper cites Gradient Surgery for Multi-Task Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Gradient Surgery for Multi-Task Learning

Reference 70

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no resolver link, observed 2026-08-06T11:03:31.393593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:31.393593Z digest=sha256:073002e38f6bf0c4c44699d59578d42a47f19902081634e75a791307ca013616

Observation 54e0e1a9-2db9-4aba-94b0-ea133a08d417 · outbound

This paper cites Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 71

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no resolver link, observed 2026-08-06T11:03:31.574523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:31.574523Z digest=sha256:5ca2c41fbbc45878ab113330f21736b943116adb68b1e25f06f4fa97f6058848

Observation 1687d3a9-5815-4d30-ad87-3707271c1528 · outbound

This paper cites Kahrs, Carlo Sferrazza, Yuval Tassa, and Pieter Abbeel.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Kahrs, Carlo Sferrazza, Yuval Tassa, and Pieter Abbeel

Reference 72

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verified fuzzy
raw_fallback, observed 2026-08-06T11:03:33.804878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T11:03:31.762955Z digest=sha256:6e710e1f76e999967a1eaf60b8bd02b502e2292f9d7fe629fc73c42786e5e813

Observation 745be101-15cf-404c-8fa4-113e362d9d90 · outbound

This paper cites robosuite: A Modular Simulation Framework and Benchmark for Robot Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-06T11:03:31.888331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:31.888331Z digest=sha256:c6529203667a8110f483918f57fdfabeb6a4005c955e2d282ed6a976859bcba4

Observation a99782d8-5d50-41a7-b8e4-d33ff230ed85 · outbound

This paper cites Robot Parkour Learning.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks Robot Parkour Learning

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-06T11:03:32.055516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:32.055516Z digest=sha256:e5da9dbad4ac57fcd9b7f169e32e797df9ceb84752fb09ccaa67bfbc5e9c4358

Observation 3610a66a-7b43-46b3-a45f-9d670e20b58c · outbound

This paper cites write newline.

Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks write newline

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-06T11:03:32.220111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:03:32.220111Z digest=sha256:ef409277ed1bb2d68f453f06a2631f2d77c0c059d3d17f52539042f23c761391

Pith citing papers

Observation c1747023-19c3-4ee6-a954-0939abf28fc4 · inbound

Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents cites this paper.

Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-04T09:47:59.248127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T09:47:59.248127Z digest=sha256:503be8bfd7b6d275feeb925dc4533e25032a3eb3bb12dc6829fc47718f3963d8

Observation a1f022c6-d5d9-416e-9437-40c857ca52a9 · inbound

TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing cites this paper.

TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-13T01:52:05.702271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-13T01:48:13.679862Z digest=sha256:642d1a43dd92fb4b2bccb05ddc25e80d64d7d27362135d11b92d9b1b66834a31

Observation 320feb81-15bf-4380-b99e-8748e88b3295 · inbound

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning cites this paper.

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-07-02T11:46:55.741036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-28T03:02:59.297911Z digest=sha256:dd009fa11c8831ade491e2df2b4403d39bb0069b429dc9486fe9fe15683c3909