Paper Citation Record · LEDGER

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning

As of 14 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 5 inbound Pith citation observations for arXiv:2502.02705.

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

pith.paper-citation-record.v1
2502.02705 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:31:10.810713Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:53:09.773129Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T05:34:40.281222Z

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy13
unresolved22
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f200afa1-4088-42b5-a626-598fb834fd70 · outbound

This paper cites Further suppose ∆r = max s r(s) − mins r(s) and ∆V = max s Vs(s) − mins Vs(s) are finite.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Further suppose ∆r = max s r(s) − mins r(s) and ∆V = max s Vs(s) − mins Vs(s) are finite

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.366765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ba8189ae-c835-4de2-a693-30fa28d15dae · outbound

This paper cites Randomized Ensembled Double Q-Learning: Learning Fast Without a Model.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.639789Z

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Observation 1f0ad521-36c7-459a-9c43-a803c24533e0 · outbound

This paper cites ∞X t=0 γt¯r(st) # = V π real(s) − Vs(s0) ≥ Eρπ real(s).

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning ∞X t=0 γt¯r(st) # = V π real(s) − Vs(s0) ≥ Eρπ real(s)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.334021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a6374873-e67e-449d-b2f5-f380bb0fd588 · outbound

This paper cites Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.660450Z

Source-reported events for the cited work

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Observation 2d05657e-a2fc-4cb6-a6fe-7a3f97185d8c · outbound

This paper cites FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.671456Z

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Observation 4b4e8856-c57c-45dc-9851-1db3dc103445 · outbound

This paper cites What went wrong? closing the sim-to-real gap via differentiable causal discovery.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning What went wrong? closing the sim-to-real gap via differentiable causal discovery

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.482741Z

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Observation 31e1ca70-602d-456e-b6d9-d70ba6bdd539 · outbound

This paper cites RMA: rapid motor adaptation for legged robots.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning RMA: rapid motor adaptation for legged robots

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.468213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b420593d-7593-4074-bc39-2452b91062a2 · outbound

This paper cites DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning

Reference 13

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Observation 5819d1c4-b456-4357-947c-e70806da3f95 · outbound

This paper cites Eureka: Human-Level Reward Design via Coding Large Language Models.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Eureka: Human-Level Reward Design via Coding Large Language Models

Reference 14

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Source-reported events for the cited work

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Observation 5d417802-c356-4b0b-a75e-2116b30f597a · outbound

This paper cites Isaac gym: High performance gpu-based physics simulation for robot learning.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Isaac gym: High performance gpu-based physics simulation for robot learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.452871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6d3a13f7-8526-4076-96b7-fd3175598aab · outbound

This paper cites ASID: Active Exploration for System Identification in Robotic Manipulation.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning ASID: Active Exploration for System Identification in Robotic Manipulation

Reference 16

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Observation f607b61d-4dd9-4cce-bd01-df8e5bd3d53b · outbound

This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 17

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Source-reported events for the cited work

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Observation 9d664a4c-e269-4b2c-852f-a80d26a62428 · outbound

This paper cites Sim-to-real transfer of robotic control with dynamics randomization.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Sim-to-real transfer of robotic control with dynamics randomization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.436844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c997893d-ca5d-4f12-8ff1-c0110fae2c31 · outbound

This paper cites 14 Published as a conference paper at ICLR 2025 Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning 14 Published as a conference paper at ICLR 2025 Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.421532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 691abc2e-98b5-4e5c-a791-9e7d5ee60c29 · outbound

This paper cites A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.727780Z

Source-reported events for the cited work

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Observation 5172ce9e-65eb-4d46-80d5-5abb480c8306 · outbound

This paper cites Integrated architectures for learning, planning, and reacting based on approxi- mating dynamic programming.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Integrated architectures for learning, planning, and reacting based on approxi- mating dynamic programming

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.403276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a7d09495-6b85-43a4-b46d-889e013f183d · outbound

This paper cites Domain Randomization via Entropy Maximization.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Domain Randomization via Entropy Maximization

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.742814Z

Source-reported events for the cited work

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Observation c4fb5b0d-48b6-4735-b0cf-67483e337c62 · outbound

This paper cites Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.753680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b78bf66-7275-4f0c-94a6-2e1ccaa5869e · outbound

This paper cites Exploring Model-based Planning with Policy Networks.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Exploring Model-based Planning with Policy Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.759214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27ab88ee-2b3d-42f3-8844-7d79ac2833d4 · outbound

This paper cites Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.765781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bda9692-72c5-4e35-b572-8005643dc710 · outbound

This paper cites Language to Rewards for Robotic Skill Synthesis.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Language to Rewards for Robotic Skill Synthesis

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.776502Z

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Observation 1ddfece0-6ab0-4aea-b6bf-7c44da3370bb · outbound

This paper cites Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.781558Z

Source-reported events for the cited work

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Observation 71ae0c97-51cd-4358-a01e-b5d603cbb963 · outbound

This paper cites γH Vsim(sH ) + H−1X t=1 γtr(st) − Vsim(s0) # = E γH Vsim(sH ) − γH−1Vsim(sH−1) + γH r(sH−1) + E.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning γH Vsim(sH ) + H−1X t=1 γtr(st) − Vsim(s0) # = E γH Vsim(sH ) − γH−1Vsim(sH−1) + γH r(sH−1) + E

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.350095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b4f302e0-4a95-4672-83ea-5809449075c5 · outbound

This paper cites an unresolved cited work.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Unresolved cited work

Reference 512

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:31:11.317789Z

Source-reported events for the cited work

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Observation 4c9efcfc-060b-4a32-aa62-4a803ab023ed · outbound

This paper cites We don’t train on any simulation data during real-world fine-tuning because we empirically found it didn’t help fine-tuning performance in our settings.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning We don’t train on any simulation data during real-world fine-tuning because we empirically found it didn’t help fine-tuning performance in our settings

Reference 1536

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.297912Z

Source-reported events for the cited work

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Observation ffad0629-a6c6-4468-bb3f-66938311de73 · outbound

This paper cites doi: 10.1145/122344.122377.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning doi: 10.1145/122344.122377

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.738072Z

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Observation 9fba0ee2-6ab2-40b1-a112-85f1dd30b7f9 · outbound

This paper cites 16 Published as a conference paper at ICLR 2025 A P ROOFS Notation Recap.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning 16 Published as a conference paper at ICLR 2025 A P ROOFS Notation Recap

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.384997Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 990175b0-5743-4621-98f1-df4ffdb23c40 · outbound

This paper cites Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.747797Z

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Observation abd4174c-d514-4e8b-9ee8-4ab1c9174b25 · outbound

This paper cites Imitation Bootstrapped Reinforcement Learning.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Imitation Bootstrapped Reinforcement Learning

Reference 2016

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Observation 80f01451-24cf-4fe0-bae2-1284d03b69fb · outbound

This paper cites Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem

Reference 2017

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Observation fe82fe4d-9f49-434f-9614-cfe244d6b4e6 · outbound

This paper cites Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.665872Z

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Observation 8c2261ac-41e5-444e-8a78-21e3ab34c1f8 · outbound

This paper cites Dota 2 with Large Scale Deep Reinforcement Learning.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Dota 2 with Large Scale Deep Reinforcement Learning

Reference 2019

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Observation c79dad8c-042a-4cc1-bbbc-84b1a4888474 · outbound

This paper cites URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T11:31:10.645494Z

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Observation d004ffad-3089-484b-9a69-bcc78b15ca00 · outbound

This paper cites Yuqing Du, Olivia Watkins, Trevor Darrell, Pieter Abbeel, and Deepak Pathak.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning Yuqing Du, Olivia Watkins, Trevor Darrell, Pieter Abbeel, and Deepak Pathak

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.497464Z

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Observation f868a3a1-a76a-4d96-848b-d441a5776a05 · outbound

This paper cites ProcTHOR: Large-scale embodied AI using procedural generation.

Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning ProcTHOR: Large-scale embodied AI using procedural generation

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:31:11.513809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 9b64537e-37e9-42e4-8dd3-a89bd3ce4825 · inbound

SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training cites this paper.

SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T10:53:09.773129Z

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Observation c8bcfd32-6511-4a9f-ba0f-67ebe3691da3 · inbound

SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training cites this paper.

SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning

Reference 43

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Observation 067fe033-1ea1-4e0c-b807-4ff601eb191c · inbound

Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation cites this paper.

Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:49:54.475351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bbef72da-9f9e-42e2-be14-678f7680644e · inbound

Beyond Pixels: Learning Invariant Rewards for Real-World Robotics From a Few Demonstrations cites this paper.

Beyond Pixels: Learning Invariant Rewards for Real-World Robotics From a Few Demonstrations Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-22T05:34:40.284030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b285372f-d2df-4ee9-8cb0-5417e19f5528 · inbound

Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller cites this paper.

Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T01:41:50.557922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.