Paper Citation Record · LEDGER

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM

As of 16 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2505.10861.

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

pith.paper-citation-record.v1
2505.10861 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:05:21.477420Z

measured 61 of 61 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:25:37.918588Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

60 of 60 outbound references displayed

verified exact1
verified fuzzy12
unresolved47
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation 500b0b05-6bbd-4c6d-b30e-4e3099c7708f · outbound

This paper cites Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color

Reference 1

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Observation 945fb16b-7968-456a-8893-509d74674454 · outbound

This paper cites Reinforcement learning: Theory and algorithms.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Reinforcement learning: Theory and algorithms

Reference 2

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Observation f975919c-8866-4ea1-8313-1a83144506b7 · outbound

This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 3

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Observation 807bbdff-39eb-47b5-aca6-892ea3e9b929 · outbound

This paper cites Unsupervised State Representation Learning in Atari.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Unsupervised State Representation Learning in Atari

Reference 4

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Observation e7b764f1-0e47-4bab-bbae-5429a9dd66e3 · outbound

This paper cites Griffiths.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Griffiths

Reference 5

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Observation d6c5c746-9aae-4a49-8511-fc163fa3c56f · outbound

This paper cites Efficient Online Reinforcement Learning with Offline Data.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Efficient Online Reinforcement Learning with Offline Data

Reference 6

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Observation 1e92dbc5-5abb-4937-9fbe-2a8046b82339 · outbound

This paper cites Neuro-dynamic programming: An overview and recent results.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Neuro-dynamic programming: An overview and recent results

Reference 7

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Observation aecc0872-0d13-4558-85bb-7a2e79c1f3e8 · outbound

This paper cites Grounding llms for robot task planning using closed-loop state feedback.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Grounding llms for robot task planning using closed-loop state feedback

Reference 8

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Observation 7012707f-78d1-4f4a-a0c8-7bf0b23ccd7f · outbound

This paper cites Language models are few-shot learners.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Language models are few-shot learners

Reference 9

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Observation 803e20cd-edfe-4994-ba6e-0f4f88ca2145 · outbound

This paper cites Grounding large language models in interactive environments with online reinforcement learning.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Grounding large language models in interactive environments with online reinforcement learning

Reference 10

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Observation 6af3f052-b3ce-4e7d-bc9b-6e9753567a0c · outbound

This paper cites Efficient Sequential Decision Making with Large Language Models.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Efficient Sequential Decision Making with Large Language Models

Reference 11

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Observation 8efc4fdf-dced-47c6-98b7-1a849c8ff17c · outbound

This paper cites LMPriors: Pre-Trained Language Models as Task-Specific Priors.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM LMPriors: Pre-Trained Language Models as Task-Specific Priors

Reference 12

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Observation 74d1beda-5d17-4ad2-8bc7-b22490e7fa56 · outbound

This paper cites Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks

Reference 13

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This paper cites Guiding pretraining in reinforcement learning with large language models.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Guiding pretraining in reinforcement learning with large language models

Reference 14

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This paper cites Tree-based batch mode reinforcement learning.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Tree-based batch mode reinforcement learning

Reference 15

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Observation a48a4f33-20fb-4817-8add-79a50cc727b9 · outbound

This paper cites Soft Actor-Critic Algorithms and Applications.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Soft Actor-Critic Algorithms and Applications

Reference 16

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Observation faa31b6e-e34a-4756-9890-4308bb567c7e · outbound

This paper cites Planning Anything with Rigor: General-Purpose Zero-Shot Planning with LLM-based Formalized Programming.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Planning Anything with Rigor: General-Purpose Zero-Shot Planning with LLM-based Formalized Programming

Reference 17

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Observation e564af48-2bb2-4c83-a939-f96492fa3324 · outbound

This paper cites Deep q-learning from demonstrations.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Deep q-learning from demonstrations

Reference 18

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Observation 8ca3dfa6-a5f3-482e-a43a-1373fbd0dd2c · outbound

This paper cites 3d-llm: Injecting the 3d world into large language models.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM 3d-llm: Injecting the 3d world into large language models

Reference 19

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Observation 6f6fa83f-5aa2-4d06-8703-9c123f6831af · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM LoRA: Low-Rank Adaptation of Large Language Models

Reference 20

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Observation d578f68d-f64b-4130-8c0b-34a273fc3554 · outbound

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Visual language maps for robot navigation

Reference 21

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Observation adfed43b-9118-4cf9-b9ca-0b365f91c080 · outbound

This paper cites Inner Monologue: Embodied Reasoning through Planning with Language Models.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Inner Monologue: Embodied Reasoning through Planning with Language Models

Reference 22

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Observation 97f35f64-43b2-4862-b800-030416b775f9 · outbound

This paper cites A survey of robot intelligence with large language models.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM A survey of robot intelligence with large language models

Reference 23

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Observation 9ade9eef-bd05-4d06-82a1-7e3fd16b17a6 · outbound

This paper cites Bench llm deciders with gym translators.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Bench llm deciders with gym translators

Reference 24

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Observation aa5fcee6-4bf2-4b9f-a749-6241563b3065 · outbound

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Housekeep: Tidying virtual households using commonsense reasoning

Reference 25

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Observation a3311083-fba8-45a7-b497-08972e02ca09 · outbound

This paper cites Reinforcement learning in robotics: A survey.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Reinforcement learning in robotics: A survey

Reference 26

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Observation 0fa4467a-407f-4a72-b660-81f06ff2e7fc · outbound

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Can large language models explore in-context?

Reference 27

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Batch reinforcement learning

Reference 28

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Observation 41565e31-b181-4d8e-9d97-25c573350a16 · outbound

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Supervised pretraining can learn in-context reinforcement learning

Reference 29

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This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 30

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Code as policies: Language model programs for embodied control

Reference 31

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 32

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Physgen: Rigid-body physics-grounded image-to-video generation

Reference 33

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Observation 576cefc7-e47d-47c8-aff9-632f26c4d459 · outbound

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM A Survey of Reinforcement Learning Informed by Natural Language

Reference 34

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Eureka: Human-Level Reward Design via Coding Large Language Models

Reference 35

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Overcoming exploration in reinforcement learning with demonstrations

Reference 36

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 37

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Llamagym: Fine-tune llm agents with online reinforcement learning

Reference 39

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Mapping language models to grounded conceptual spaces

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis

Reference 41

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Neural fitted q iteration--first experiences with a data efficient neural reinforcement learning method

Reference 42

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Kickstarting Deep Reinforcement Learning

Reference 43

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM d3rlpy: An offline deep reinforcement learning library

Reference 44

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Mastering the game of go without human knowledge

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM It is Too Many Options: Pitfalls of Multiple-Choice Questions in Generative AI and Medical Education

Reference 46

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

Reference 47

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM True Knowledge Comes from Practice: Aligning LLMs with Embodied Environments via Reinforcement Learning

Reference 48

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 49

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Representation Learning for Online and Offline RL in Low-rank MDPs

Reference 50

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Deep Reinforcement Learning with Double Q-learning

Reference 51

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Instabilities of offline rl with pre-trained neural representation

Reference 52

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Chain-of-thought prompting elicits reasoning in large language models

Reference 53

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Policy finetuning: Bridging sample-efficient offline and online reinforcement learning

Reference 54

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Text2Reward: Reward Shaping with Language Models for Reinforcement Learning

Reference 55

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Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Qwen2.5 Technical Report

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:21.459606Z

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Observation 9b2129aa-1500-4fb9-b8a8-401021eb3933 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM ReAct: Synergizing Reasoning and Acting in Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:21.463838Z

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Observation 41044195-3ce1-4045-a71f-decbeb0082d3 · outbound

This paper cites Policy finetuning in reinforcement learning via design of experiments using offline data.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Policy finetuning in reinforcement learning via design of experiments using offline data

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:22.114177Z

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Observation a4504fb6-8505-49b8-b05a-66dc89d85d30 · outbound

This paper cites Online decision transformer.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Online decision transformer

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:22.097815Z

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Observation 2e7e81b9-3538-4909-9f76-5aa26cd04b72 · outbound

This paper cites Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models.

Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:21.477420Z

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Pith citing papers

Observation f2d30702-5654-451e-a9d5-872b374fdef9 · inbound

ProDVI: Programmatic Dynamics Priors for Value Network Initialization cites this paper.

ProDVI: Programmatic Dynamics Priors for Value Network Initialization Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:25:38.695370Z

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