Paper Citation Record · LEDGER

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks

As of 17 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2509.14380.

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

pith.paper-citation-record.v1
2509.14380 v3

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

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measured 56 of 56 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

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External citation measurements

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Outbound references

Observation bfd348d9-9681-42b9-b4f9-0ea1c7772c1c · outbound

This paper cites Grandmaster level in starcraft ii using multi-agent reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Grandmaster level in starcraft ii using multi-agent reinforcement learning,

Reference 1

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Observation 8d2de1c7-af5c-48fa-a2cb-eb56b88909f7 · outbound

This paper cites Smacv2: An improved benchmark for cooperative multi-agent reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Smacv2: An improved benchmark for cooperative multi-agent reinforcement learning,

Reference 2

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Observation c984be24-13f0-41d6-b48e-e82efe451f73 · outbound

This paper cites Google research football: A novel reinforcement learning environ- ment,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Google research football: A novel reinforcement learning environ- ment,

Reference 3

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Observation ca312782-fff8-43b2-903c-a1bca889987c · outbound

This paper cites Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams

Reference 4

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This paper cites Marladona-towards cooperative team play using multi-agent reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Marladona-towards cooperative team play using multi-agent reinforcement learning,

Reference 5

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Observation 04f420dd-d8b7-479f-89c6-215b3b86a852 · outbound

This paper cites Leveraging large language models for effective and explainable multi-agent credit assignment,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Leveraging large language models for effective and explainable multi-agent credit assignment,

Reference 6

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Observation 80e48075-02d2-459d-920d-fe6e694fbc23 · outbound

This paper cites Variational automatic curriculum learning for sparse-reward cooperative multi-agent problems,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Variational automatic curriculum learning for sparse-reward cooperative multi-agent problems,

Reference 7

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Observation 41dd963c-17e7-487e-b7af-4edb3bcfad14 · outbound

This paper cites Au- tomatic curriculum learning for deep rl: a short survey,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Au- tomatic curriculum learning for deep rl: a short survey,

Reference 8

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Observation 3bec0d51-1699-44b7-a108-34f3e6946c81 · outbound

This paper cites Cooperative multi- agent control using deep reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Cooperative multi- agent control using deep reinforcement learning,

Reference 9

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Observation 82e1ea03-1f27-46d0-9d72-56eb9603a1b4 · outbound

This paper cites A survey on curriculum learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks A survey on curriculum learning,

Reference 10

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Observation 53f08a60-243d-4b3d-861c-aa2ff4efb29f · outbound

This paper cites V oyager: An open-ended embodied agent with large language models,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks V oyager: An open-ended embodied agent with large language models,

Reference 11

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This paper cites Progprompt: Generating situated robot task plans using large language models,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Progprompt: Generating situated robot task plans using large language models,

Reference 12

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Observation c7198cff-ef98-401e-b71d-5c8e6dedca4b · outbound

This paper cites Eureka: Human-level reward design via coding large language models,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Eureka: Human-level reward design via coding large language models,

Reference 13

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Observation 5999ba2b-cb4e-4228-9f0c-153f1dc8e89c · outbound

This paper cites Vision-language models are zero-shot reward models for reinforce- ment learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Vision-language models are zero-shot reward models for reinforce- ment learning,

Reference 14

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Observation 3f031d1d-daf4-4e6e-82b3-412779c604f1 · outbound

This paper cites AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World

Reference 15

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Observation 8930a0ac-e4dd-4b45-97a6-cc10215b69b4 · outbound

This paper cites AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation

Reference 16

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Observation 43e83483-c8bc-4b55-b801-2f22a2eb5a5f · outbound

This paper cites Reverse forward curriculum learning for extreme sample and demo efficiency,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Reverse forward curriculum learning for extreme sample and demo efficiency,

Reference 17

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Observation eb2eed43-a4d1-4eb2-ac41-7f3bd4b63424 · outbound

This paper cites Unsupervised curricula for visual meta-reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Unsupervised curricula for visual meta-reinforcement learning,

Reference 18

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Observation 9bf021fa-cc3f-44cd-9588-adcb8646f87b · outbound

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Tizero: Mastering multi-agent football with curriculum learning and self-play,

Reference 19

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Observation f33599f5-fe9e-4b02-8a5d-e40a578b5e86 · outbound

This paper cites Curricullm: Automatic task curricula design for learning complex robot skills using large language models,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Curricullm: Automatic task curricula design for learning complex robot skills using large language models,

Reference 20

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Environment curriculum generation via large language models,

Reference 21

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Observation cf87b1ef-6d6b-4f13-a139-328a55e62497 · outbound

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Aura: Agentic upskilling via reinforced abstractions,

Reference 22

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This paper cites Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics

Reference 23

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Observation a8135466-0772-4fd3-a28b-dc8030fee3fa · outbound

This paper cites Learning a High-quality Robotic Wiping Policy Using Systematic Reward Analysis and Visual-Language Model Based Curriculum.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Learning a High-quality Robotic Wiping Policy Using Systematic Reward Analysis and Visual-Language Model Based Curriculum

Reference 24

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Learning multi-agent loco-manipulation for long-horizon quadrupedal pushing,

Reference 25

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Observation 47e29ed9-b3e5-461e-93e1-47661611efa2 · outbound

This paper cites Decentralized Navigation of a Cable-Towed Load using Quadrupedal Robot Team via MARL.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Decentralized Navigation of a Cable-Towed Load using Quadrupedal Robot Team via MARL

Reference 26

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Resolving conflicting constraints in multi-agent rein- forcement learning with layered safety,

Reference 27

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Observation 9b8a5644-0d74-4b43-90d2-e2156ecce97e · outbound

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Learning differentiable and safe multi-robot control for generalization to novel environments using control barrier functions,

Reference 28

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks The surprising effectiveness of ppo in cooperative multi-agent games,

Reference 29

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Monotonic value function factorisation for deep multi- agent reinforcement learning,

Reference 30

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Lever- aging pre-trained large language models to construct and utilize world models for model-based task planning,

Reference 31

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,

Reference 32

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks 3d-vla: A 3d vision-language-action generative world model,

Reference 33

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This paper cites Synthesizing interpretable control poli- cies through large language model guided search,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Synthesizing interpretable control poli- cies through large language model guided search,

Reference 34

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Large language model based multi-agents: a survey of progress and challenges,

Reference 35

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents

Reference 36

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Loss of plasticity in continual deep reinforcement learning,

Reference 37

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Loss of plasticity in deep continual learning,

Reference 38

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Mqe: Unleashing the power of interaction with multi-agent quadruped envi- ronment,

Reference 39

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

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

Reference 40

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks OpenRL: A Unified Reinforcement Learning Framework

Reference 41

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Observation 63c55479-751d-4232-beda-e983aa341a1d · outbound

This paper cites skrl: Modular and flexible library for reinforcement learning,.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks skrl: Modular and flexible library for reinforcement learning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.082715Z

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This paper cites One for generating candidate curricula, and another for refining the final curriculum from the candidates.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks One for generating candidate curricula, and another for refining the final curriculum from the candidates

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.070532Z

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This paper cites Prompt 3: LLM prompt for generating base reward function.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Prompt 3: LLM prompt for generating base reward function

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.058085Z

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Prompt 4: VLM prompt for policy evaluation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.045528Z

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CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Unresolved cited work

Reference 46

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This paper cites very close.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks very close

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.021042Z

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Observation 7cb458cd-b881-466d-b5f9-04b9130e812f · outbound

This paper cites One for generating advice on how to refine the reward (VLM), and one for refining the reward function given the advice (LLM).

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks One for generating advice on how to refine the reward (VLM), and one for refining the reward function given the advice (LLM)

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:26.009147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 27888c29-f4c7-44de-ad69-98b7fa1811cc · outbound

This paper cites reach_reward.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks reach_reward

Reference 49

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:54:25.997016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation db39c5b9-9b97-4560-b0b6-11375773a7d0 · outbound

This paper cites Because the agents rarely reach that threshold early on, they get almost zero signal to lift beyond ~0.016 m.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Because the agents rarely reach that threshold early on, they get almost zero signal to lift beyond ~0.016 m

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:25.984465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9acaca1d-9d2b-4513-9d3a-165aa5292ea5 · outbound

This paper cites an unresolved cited work.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:54:25.970937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7aea6525-0f93-46ff-95fc-5475e0ba5245 · outbound

This paper cites Once the handles are touched or grasped, there is little extra push to actually raise the pot.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks Once the handles are touched or grasped, there is little extra push to actually raise the pot

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:25.958144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bd2ebe99-9204-4922-a3b8-b0b6caf39e59 · outbound

This paper cites This gives gradient toward any increase in height, not just surpassing 0.1 m.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks This gives gradient toward any increase in height, not just surpassing 0.1 m

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:25.945421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f8015b13-809b-4d46-924a-ccf9300627fd · outbound

This paper cites This still rewards low tilt but provides a gradient that gently pushes the pot back toward upright whenever it begins to tilt.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks This still rewards low tilt but provides a gradient that gently pushes the pot back toward upright whenever it begins to tilt

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:54:25.932738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 70989a4b-1106-4453-bd86-8174867af71d · outbound

This paper cites touch and release.

CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks touch and release

Reference 55

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:54:25.919759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation e3b92c80-6ea9-4bc6-855f-3eaddf39fdb3 · inbound

MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation cites this paper.

MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks

Reference 77

Resolution: unresolved; no resolver link, observed 2026-07-31T21:55:17.414682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.