Paper Citation Record · LEDGER

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.20671.

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

pith.paper-citation-record.v1
2505.20671 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:53:42.129182Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact2
verified fuzzy14
unresolved23
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d817240d-38a3-4af2-b1f8-cc32045505ad · outbound

This paper cites Reincarnating reinforcement learning: Reusing prior computation to accelerate progress.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Reincarnating reinforcement learning: Reusing prior computation to accelerate progress

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:37.139343Z

Source-reported events for the cited work

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Observation f8430a45-e6ef-49df-9ff3-396f11e51c05 · outbound

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

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:37.218896Z

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Observation 22846068-a9f4-436b-98d3-e1a47af968fe · outbound

This paper cites OpenAI Gym.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation OpenAI Gym

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:37.377259Z

Source-reported events for the cited work

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Observation 670978e9-6d84-46a8-99d0-96f2821570bf · outbound

This paper cites Exploration by Random Network Distillation.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Exploration by Random Network Distillation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:37.484831Z

Source-reported events for the cited work

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Observation 62ebb324-b823-48ac-91bf-b85644f567ce · outbound

This paper cites Imitation learning from vague feedback.Advances in Neural Information Processing Systems, 36:48275–48292, 2023.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Imitation learning from vague feedback.Advances in Neural Information Processing Systems, 36:48275–48292, 2023

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:48.333620Z

Source-reported events for the cited work

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Observation e536404e-ff62-4905-96ff-13b9d3dd7b60 · outbound

This paper cites Statemask: Explaining deep reinforcement learning through state mask.Advances in Neural Information Processing Systems, 36:62457–62487, 2023.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Statemask: Explaining deep reinforcement learning through state mask.Advances in Neural Information Processing Systems, 36:62457–62487, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:48.142610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 65fddf26-7569-45a7-9c3f-12e0a2611be7 · outbound

This paper cites RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:53:43.193960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3174fb9c-6810-4c86-b4d3-fe463fc0f735 · outbound

This paper cites Using Natural Language for Reward Shaping in Reinforcement Learning.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Using Natural Language for Reward Shaping in Reinforcement Learning

Reference 8

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Source-reported events for the cited work

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Observation 1b974b84-8aec-4df4-ba8d-14f2524b65c8 · outbound

This paper cites an unresolved cited work.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:47.935363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e34f203c-d287-4e5f-80d6-ce50ded01619 · outbound

This paper cites Edge: Explaining deep reinforcement learning policies.Advances in Neural Information Processing Systems, 34:12222–12236, 2021.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Edge: Explaining deep reinforcement learning policies.Advances in Neural Information Processing Systems, 34:12222–12236, 2021

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:47.739766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ba18b94e-e4b3-42c1-982e-8ea44066974b · outbound

This paper cites Uncertainty-aware reinforcement learning for autonomous driving with multimodal digital driver guidance.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Uncertainty-aware reinforcement learning for autonomous driving with multimodal digital driver guidance

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:47.568713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4812f92e-cc31-4232-831c-b9f281382fe4 · outbound

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

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Inner Monologue: Embodied Reasoning through Planning with Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:38.626574Z

Source-reported events for the cited work

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Observation e2c8dac2-a844-4e4f-a062-14f1f34ff23c · outbound

This paper cites Reward Design with Language Models.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Reward Design with Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:38.843038Z

Source-reported events for the cited work

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Observation 693244ae-1063-432e-a529-52d44d492358 · outbound

This paper cites A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:38.995783Z

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Unavailable: canonical work link unavailable.

Observation 12681bee-52c3-4902-933a-a418ab9f68cb · outbound

This paper cites Traj-llm: A new exploration for empowering trajectory prediction with pre-trained large language models.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Traj-llm: A new exploration for empowering trajectory prediction with pre-trained large language models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:47.327917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2713381e-02ec-4d0b-b4b3-831dc1b409e1 · outbound

This paper cites Pre-trained language models for interactive decision-making.Advances in Neural Information Processing Systems, 35:31199–31212, 2022.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Pre-trained language models for interactive decision-making.Advances in Neural Information Processing Systems, 35:31199–31212, 2022

Reference 16

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Source-reported events for the cited work

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Observation ee228267-291e-4941-8d23-1deac2a35ed2 · outbound

This paper cites Utility: Utilizing explainable reinforcement learning to improve reinforcement learning.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Utility: Utilizing explainable reinforcement learning to improve reinforcement learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:47.105048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation db71e279-304a-40eb-babd-6ef30e623a54 · outbound

This paper cites Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:53:42.823242Z

Source-reported events for the cited work

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Observation a91e3f3f-b9af-4f4d-82a7-4c258e4dd159 · outbound

This paper cites Episodic Curiosity through Reachability.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Episodic Curiosity through Reachability

Reference 19

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Source-reported events for the cited work

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Observation 01195cea-d6ab-40a3-a614-170d2368343d · outbound

This paper cites Proximal Policy Optimization Algorithms.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Proximal Policy Optimization Algorithms

Reference 20

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Observation 06068cad-f92a-42ea-8564-3df367bfa8d6 · outbound

This paper cites Perceiver-actor: A multi-task transformer for robotic manipulation.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Perceiver-actor: A multi-task transformer for robotic manipulation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:39.913463Z

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Observation 260cef10-6f61-4bbd-9f6a-b10b75bc6283 · outbound

This paper cites Joint rebalancing and charging for shared electric micromobility vehicles with energy-informed demand.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Joint rebalancing and charging for shared electric micromobility vehicles with energy-informed demand

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:46.732568Z

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Observation 3c59896d-b416-4b67-bc3b-ffc9097a5b5e · outbound

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

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Mujoco: A physics engine for model-based control

Reference 23

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Observation 02e1134c-1b30-4393-9a81-ec0766101ccc · outbound

This paper cites Correct me if i’m wrong: Using non-experts to repair reinforcement learning policies.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Correct me if i’m wrong: Using non-experts to repair reinforcement learning policies

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:46.458670Z

Source-reported events for the cited work

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Observation aeeaff42-2518-49ff-9fcc-b8685f8bdd45 · outbound

This paper cites Grandmaster level in starcraft ii using multi-agent reinforcement learning.nature, 575(7782):350–354, 2019.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Grandmaster level in starcraft ii using multi-agent reinforcement learning.nature, 575(7782):350–354, 2019

Reference 25

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Observation 52c22c42-2152-43e2-87f6-f02544f078e2 · outbound

This paper cites STeCa: Step-level Trajectory Calibration for LLM Agent Learning.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation STeCa: Step-level Trajectory Calibration for LLM Agent Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:40.544944Z

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Observation afa650bf-3544-4a5a-b0ec-b02436189bff · outbound

This paper cites Read and reap the rewards: Learning to play atari with the help of instruction manuals.Advances in Neural Information Processing Systems, 36:1009–1023, 2023.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Read and reap the rewards: Learning to play atari with the help of instruction manuals.Advances in Neural Information Processing Systems, 36:1009–1023, 2023

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:46.197516Z

Source-reported events for the cited work

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Observation 99f315af-1619-4fde-a573-5a9787d0ae87 · outbound

This paper cites Keep CALM and Explore: Language Models for Action Generation in Text-based Games.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Keep CALM and Explore: Language Models for Action Generation in Text-based Games

Reference 28

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Observation 100c3049-4c9c-406e-9c5d-55f8c72b669d · outbound

This paper cites Offline Imitation Learning Through Graph Search and Retrieval.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Offline Imitation Learning Through Graph Search and Retrieval

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:40.827370Z

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Observation 846a6096-7ead-4efe-b425-75e07df69db1 · outbound

This paper cites {AIRS}: Expla- nation for deep reinforcement learning based security applications.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation {AIRS}: Expla- nation for deep reinforcement learning based security applications

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:45.933464Z

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Observation af353fed-4776-48f1-b44b-244220b05b17 · outbound

This paper cites Language to Rewards for Robotic Skill Synthesis.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Language to Rewards for Robotic Skill Synthesis

Reference 31

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Observation 06958167-1ff9-409e-a8d3-763007f96967 · outbound

This paper cites + str(e) +.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation + str(e) +

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:45.573248Z

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Observation 7534699e-44c3-4777-affd-0503001fe5ef · outbound

This paper cites We should introduce a mechanism to reinforce learning during critical times while undermining actions leading to unfavorable outcomes.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation We should introduce a mechanism to reinforce learning during critical times while undermining actions leading to unfavorable outcomes

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:45.259951Z

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Observation 3534af9b-9eb9-495c-aef9-08c1edb9c65d · outbound

This paper cites The policy should focus on maintaining positional advantage to intercept the ball efficiently.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation The policy should focus on maintaining positional advantage to intercept the ball efficiently

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:44.902909Z

Source-reported events for the cited work

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Observation 6cc0f9ff-df86-4f86-b4db-b42a43767c48 · outbound

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LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:44.549204Z

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Observation 93396c86-d22d-4fff-aa84-14918688c819 · outbound

This paper cites an unresolved cited work.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:44.293375Z

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Observation 586c101f-69b3-4154-81a5-a5d5b6186390 · outbound

This paper cites Implementing a mechanism that prioritizes actions based on proximity to the ball in the x-coordinates can significantly improve action selection during critical moments.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Implementing a mechanism that prioritizes actions based on proximity to the ball in the x-coordinates can significantly improve action selection during critical moments

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:43.976039Z

Source-reported events for the cited work

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Observation d72a2abc-3c3f-4519-8eb0-cd57a5fa90e9 · outbound

This paper cites an unresolved cited work.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:43.797107Z

Source-reported events for the cited work

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Observation 38ef5141-ee5b-463d-9b99-8f1a0e6391c9 · outbound

This paper cites an unresolved cited work.

LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:43.615588Z

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