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Paper Citation Record · LEDGER

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing

As of 10 August 2026, this Paper Citation Record lists 100 of 113 outbound references and 0 inbound Pith citation observations for arXiv:2509.08329.

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

pith.paper-citation-record.v1
2509.08329 v1

Coverage vector

measured 100 of 113 reference resolution

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measured 100 of 100 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

100 of 113 outbound references displayed

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Outbound references

Observation 97998644-e4a1-4020-a550-a00f6d742ba2 · outbound

This paper cites Leveraging more of biology in evolutionary reinforcement learning.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Leveraging more of biology in evolutionary reinforcement learning

Reference 1

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Observation c79f6e3a-6f0c-4178-9e7f-8170e7c57446 · outbound

This paper cites Reinforcement learning: A survey.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Reinforcement learning: A survey

Reference 2

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This paper cites Model-based reinforcement learning: A survey.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Model-based reinforcement learning: A survey

Reference 3

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Observation 820a1662-99eb-4d92-b9ca-db254e92371f · outbound

This paper cites Implementation of Q-Learning algorithm for solving maze prob- lem.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Implementation of Q-Learning algorithm for solving maze prob- lem

Reference 5

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Observation b2731e05-0d63-49c3-ae8f-e6780a86f9fd · outbound

This paper cites Sequence learning: From recognition and prediction to sequential decision making.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Sequence learning: From recognition and prediction to sequential decision making

Reference 6

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Observation bf08b2d6-0a04-4df6-abf1-3e1b1a14c4ec · outbound

This paper cites Deep reinforcement learning for sequence-to-sequence models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Deep reinforcement learning for sequence-to-sequence models

Reference 7

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Observation 7e868f39-1469-4e0e-8d56-3512a90b8b41 · outbound

This paper cites Adaptive look-ahead economic dispatch based on deep reinforcement learning.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Adaptive look-ahead economic dispatch based on deep reinforcement learning

Reference 9

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Observation 382bac6f-afd6-418c-8d7b-5b2176d1caf2 · outbound

This paper cites Recent developments of game theory and reinforce- ment learning approaches: A systematic review.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Recent developments of game theory and reinforce- ment learning approaches: A systematic review

Reference 10

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Observation 850b7ca9-92ae-44e6-80db-9f70a33b25a9 · outbound

This paper cites Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey

Reference 11

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Observation 9efd5625-d009-4fa2-bf0f-8182676441b7 · outbound

This paper cites Rein- forcement learning for autonomous process control in industry 4.0: Advantages and challenges.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Rein- forcement learning for autonomous process control in industry 4.0: Advantages and challenges

Reference 12

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Observation 616f27f5-48e6-4a55-be90-9af4abd9da0e · outbound

This paper cites A review of safe reinforcement learning: Methods, theories and applications.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing A review of safe reinforcement learning: Methods, theories and applications

Reference 13

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Observation 56a2af26-ba0a-43bd-a36f-becdce148d43 · outbound

This paper cites Distributed deep reinforcement learning based gradient quantization for federated learning enabled vehi- cle edge computing.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Distributed deep reinforcement learning based gradient quantization for federated learning enabled vehi- cle edge computing

Reference 14

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Observation 5bde60e4-7b2c-4bbc-b1df-ad5dce53b398 · outbound

This paper cites A review of research on reinforcement learning algorithms for multi-agents.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing A review of research on reinforcement learning algorithms for multi-agents

Reference 15

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Observation bb36baed-bee1-4509-9533-f174a228d9b0 · outbound

This paper cites Toward enhanced reinforcement learning-based resource management via digital twin: Opportunities, applications, and challenges.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Toward enhanced reinforcement learning-based resource management via digital twin: Opportunities, applications, and challenges

Reference 16

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Observation 4a718f5b-19d3-4b29-9a15-f13c145ebb75 · outbound

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Human-level control through deep reinforcement learning

Reference 17

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Observation c207e56b-9b43-485d-9dfc-fe9d160967de · outbound

This paper cites Evolu- tionary reinforcement learning: a systematic review and future directions.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Evolu- tionary reinforcement learning: a systematic review and future directions

Reference 18

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Observation 6c017dfb-3f69-4f08-90ca-fae28ecfb380 · outbound

This paper cites RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration

Reference 19

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Observation 37eb84f2-8d3a-4e10-afa7-b5768e6658d0 · outbound

This paper cites A Reinforcement Learning Training Acceleration Method Based on Knowledge Distillation.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing A Reinforcement Learning Training Acceleration Method Based on Knowledge Distillation

Reference 20

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Observation ef49b158-ab05-4ac1-bbbf-565c6e550954 · outbound

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Plan-based reward shaping for reinforcement learning

Reference 21

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Observation 6279c055-d5d8-495c-907a-9d83eb08c3c8 · outbound

This paper cites Cur- riculum learning for reinforcement learning domains: A framework and survey.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Cur- riculum learning for reinforcement learning domains: A framework and survey

Reference 22

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Observation bda7df39-dbd7-42f9-92e9-eb3f2758db26 · outbound

This paper cites Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning

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Observation 8e3be7d4-cfe9-4dca-9b49-c9c20463f502 · outbound

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Meta-learning in reinforcement learning

Reference 24

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Observation a83b7b2c-630d-41c5-8d9e-db6c08b215e3 · outbound

This paper cites How Can LLM Guide RL? A Value-Based Approach.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing How Can LLM Guide RL? A Value-Based Approach

Reference 25

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Observation cf13cf78-cf9a-4331-8e3c-a3fba642522a · outbound

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing GPT-4 Technical Report

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Observation e52e8950-48be-4834-b1c4-cc78dd31c809 · outbound

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning

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Observation 913436a7-c629-4377-b252-e8cf67bbe361 · outbound

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Teacher-Student Architecture for Knowledge Distillation: A Survey

Reference 29

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Towards Generalizable Agents in Text-Based Educational Environments: A Study of Integrating RL with LLMs

Reference 30

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Intelligent Control of Closed-Loop Sedation in Simulated ICU Patients

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Unresolved cited work

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Unresolved cited work

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing A reinforcement learning approach to obtain treatment strategies in sequential medi- cal decision problems

Reference 34

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Observation 53a8f259-8ef5-400f-a719-c7ccf1e195ef · outbound

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Rein- forcement Learning for Closed-Loop Propofol Anesthesia: A Study in Human V olunteers

Reference 35

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing 7. Using Reinforcement Learning in the Algorithmic Trading Problem

Reference 36

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Deep Reinforcement Learning for Optimizing Finance Portfolio Man- agement

Reference 37

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Observation c53ff995-c701-4e02-8882-a2275dbdb1ab · outbound

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Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Reinforcement Learning for Solving the Vehicle Routing Problem

Reference 38

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Observation 2c21783d-75c7-4b29-975f-3a8c51007b14 · outbound

This paper cites Reinforcement Learning Boat Autopilot: A Sample-efficient and Model Predictive Control based Approach.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Reinforcement Learning Boat Autopilot: A Sample-efficient and Model Predictive Control based Approach

Reference 39

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Observation 54991b6d-13a6-4817-a373-dd84d3210b0b · outbound

This paper cites Sutton and Andrew G.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Sutton and Andrew G

Reference 40

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Observation e0e642b9-ee61-4a04-996d-35055efbedef · outbound

This paper cites Reinforcement learning in artificial and biological systems.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Reinforcement learning in artificial and biological systems

Reference 41

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Observation 0668dfd1-9c6f-4d29-a824-321a3255a638 · outbound

This paper cites Introduction to reinforcement learning.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Introduction to reinforcement learning

Reference 42

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source=pdf_text observed=2026-08-04T20:49:37.737230Z digest=sha256:bc58d7fdea6a5e5fd87ff372137e318342a380f28ceb8eb339de7eece5dd82d4

Observation 4b58e068-7a7c-4c2f-8d16-18e029473e81 · outbound

This paper cites A generalized reinforcement-learning model: Convergence and appli- cations.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing A generalized reinforcement-learning model: Convergence and appli- cations

Reference 43

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source=pdf_text observed=2026-08-04T20:49:37.740802Z digest=sha256:b39b556dedf6c3b8a73772b4c35687a20ea2243cb5c843ec4e0f57a25bdeac2e

Observation ccf8d915-cb40-4d7d-98b0-323acc5edf54 · outbound

This paper cites Q-learning.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Q-learning

Reference 44

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source=pdf_text observed=2026-08-04T20:49:37.743964Z digest=sha256:f017d40637305f8220320de33c72211f020fb32ceb0e5473c42d91a9ef70ba3b

Observation 8b5c1543-1014-43c2-97c2-b3a5d7bdbaba · outbound

This paper cites Value-free reinforcement learning: policy optimization as a minimal model of operant behavior.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Value-free reinforcement learning: policy optimization as a minimal model of operant behavior

Reference 45

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source=pdf_text observed=2026-08-04T20:49:37.747016Z digest=sha256:f4b30c282b4a883cbc2df62cbe5d6752bcc2ed94e900dc6e07ca196560566fdd

Observation 2b0fb647-960b-40ae-96f3-0d4afa374a99 · outbound

This paper cites Actor-Critic Reinforcement Learning and Ap- plication in Developing Computer-Vision-Based Interface Tracking.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Actor-Critic Reinforcement Learning and Ap- plication in Developing Computer-Vision-Based Interface Tracking

Reference 46

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source=pdf_text observed=2026-08-04T20:49:37.750197Z digest=sha256:30c6eb3023b2232dba3abd1b67a7f598a1aa23fbf9c1bc89fa68060ba4bbc995

Observation 839563fb-30a3-40a9-879d-a59e2a374504 · outbound

This paper cites an unresolved cited work.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Unresolved cited work

Reference 47

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source=pdf_text observed=2026-08-04T20:49:37.753240Z digest=sha256:81bed3da0019d71710df9e946801396223d5bcd083accb5976767a504df02478

Observation 7a4a842e-b2c9-4199-a853-d628c09366ae · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Gemini: A Family of Highly Capable Multimodal Models

Reference 48

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source=pdf_text observed=2026-08-04T20:49:37.756531Z digest=sha256:ae8a7aa8a9a02b271d7b3649b4d1cc7aca27ceb80f71485ed92f37e84fd05b7a

Observation d7f5c7ec-2cb9-4311-a3c5-9a50cccdc799 · outbound

This paper cites Large Language Models: A Survey.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Large Language Models: A Survey

Reference 49

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Observation c73edb9b-9207-47b4-a80d-777ac9b2c41d · outbound

This paper cites A Survey of Large Language Models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing A Survey of Large Language Models

Reference 50

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source=pdf_text observed=2026-08-04T20:49:37.763648Z digest=sha256:17e1c185afcd89f52890bd81335b00cb8e3bdf54d8d42c280b394b07377b1f4a

Observation 169ab558-0904-4eea-bb73-fc619308b5ed · outbound

This paper cites Large language models surpass human experts in predicting neuroscience results.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Large language models surpass human experts in predicting neuroscience results

Reference 51

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Observation 485da6c9-08c4-4a10-8cbf-b76a49004695 · outbound

This paper cites How Much Knowledge Can You Pack Into the Parameters of a Language Model?.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing How Much Knowledge Can You Pack Into the Parameters of a Language Model?

Reference 52

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Observation 9c18f6c0-8fca-4bcd-ae23-f34ed48ba83b · outbound

This paper cites Analyzing Commonsense Emergence in Few-shot Knowledge Models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Analyzing Commonsense Emergence in Few-shot Knowledge Models

Reference 53

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source=pdf_text observed=2026-08-04T20:49:37.773737Z digest=sha256:4b345d691804c646178d3d0468df2eb478df58c2a78857533d5424078e9c7a9c

Observation 0b5c305f-85f3-488f-adda-02849442b081 · outbound

This paper cites Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction

Reference 54

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Observation 9f8804ac-45b9-4662-98e4-939c42c56458 · outbound

This paper cites TALLRec: An Effec- tive and Efficient Tuning Framework to Align Large Language Model with Recommendation.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing TALLRec: An Effec- tive and Efficient Tuning Framework to Align Large Language Model with Recommendation

Reference 55

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Observation 8eea794a-f34f-4b0f-871a-1ac091ca6bd7 · outbound

This paper cites LLM-Rec: Personalized Recommendation via Prompting Large Language Models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing LLM-Rec: Personalized Recommendation via Prompting Large Language Models

Reference 56

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source=pdf_text observed=2026-08-04T20:49:37.783309Z digest=sha256:191e7ee28d98a7b29d40612e7c7ef95c342149e4c568355e9f16166763290c26

Observation 71897617-f03c-4e26-b5b0-151f869fecf6 · outbound

This paper cites Review-driven Personalized Preference Reasoning with Large Language Models for Recommendation.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Review-driven Personalized Preference Reasoning with Large Language Models for Recommendation

Reference 57

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source=pdf_text observed=2026-08-04T20:49:37.786263Z digest=sha256:cbb10296d65642fad2fb1a23c6a5e1728155206e9aa260f01f220959e1be63b4

Observation c677e816-9da8-457b-8bb7-e528754de729 · outbound

This paper cites Wordcraft: Story Writing With Large Lan- guage Models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Wordcraft: Story Writing With Large Lan- guage Models

Reference 58

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Observation e341a6e8-33f9-4a6f-a4c2-604358fccd77 · outbound

This paper cites A comprehensive survey on integrating large language models with knowledge-based methods.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing A comprehensive survey on integrating large language models with knowledge-based methods

Reference 59

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Observation 320b0594-c9cb-4101-9eb6-3d784beb10ea · outbound

This paper cites Large Language Models for Scientific Synthesis, Inference and Explanation.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Large Language Models for Scientific Synthesis, Inference and Explanation

Reference 60

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Observation f258a823-2377-476b-86e8-218e3598e0d6 · outbound

This paper cites Leveraging Encoder-only Large Language Models for Mobile App Review Feature Extraction.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Leveraging Encoder-only Large Language Models for Mobile App Review Feature Extraction

Reference 61

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source=pdf_text observed=2026-08-04T20:49:37.804114Z digest=sha256:e1f37390fd8dd4d9202fd570b31ec37a44eccbdd1602ae6d95037b12da8b1def

Observation d52c73ad-74e1-4e7d-bcd0-a80835ca7701 · outbound

This paper cites Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges

Reference 62

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Observation 167ab8dd-bad4-450b-9ae0-7a8b773e333e · outbound

This paper cites Large Language Models as Data Preprocessors.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Large Language Models as Data Preprocessors

Reference 63

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source=pdf_text observed=2026-08-04T20:49:37.811300Z digest=sha256:51821a9a84744d9a94ac163976b1a3a4d35221d03a578b6bb63c5a3273679c19

Observation c25e1175-70f2-4f7b-994b-d805a28f3250 · outbound

This paper cites DiarizationLM: Speaker Diarization Post-Processing with Large Language Models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing DiarizationLM: Speaker Diarization Post-Processing with Large Language Models

Reference 64

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.815064Z digest=sha256:a539dee2181a4fcdcf11a4db8e515598c3f85182243d4f47ab883da9135df64d

Observation 52f1937a-41ac-4b9b-8cb2-d023b6eb67c9 · outbound

This paper cites Data Augmentation for Intent Classification with Off-the-shelf Large Language Models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Data Augmentation for Intent Classification with Off-the-shelf Large Language Models

Reference 65

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source=pdf_text observed=2026-08-04T20:49:37.818378Z digest=sha256:ae80f94cf3ec75d18307fb32970c16d91e88241e92267311cc9a7f105c59135a

Observation 7b17a9e9-a00a-4fb3-b3f6-3743389564d0 · outbound

This paper cites When and how to paraphrase for named entity recognition?.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing When and how to paraphrase for named entity recognition?

Reference 66

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source=pdf_text observed=2026-08-04T20:49:37.821853Z digest=sha256:190fc8aeaf2d8aa0c1fd87c36e736ded6e8e3c99e864dda75d4411f146066dd9

Observation 2d5c61ac-b1b9-4897-b28f-e07023de6692 · outbound

This paper cites Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?

Reference 67

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Observation 471b3a71-5314-4d8c-92b9-b09b593b1b8c · outbound

This paper cites The Llama 3 Herd of Models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing The Llama 3 Herd of Models

Reference 68

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source=pdf_text observed=2026-08-04T20:49:37.828512Z digest=sha256:3534571e1d822119762453266d73eaea0a4a551ab999eb8b0dfc0b0778b80009

Observation 49b0f118-b64e-4178-9d88-ddb33e141722 · outbound

This paper cites an unresolved cited work.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Unresolved cited work

Reference 69

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source=pdf_text observed=2026-08-04T20:49:37.831676Z digest=sha256:027967bbc462fb0a57ce655866c22b5e11179f1e27a67a85f084aedaa3b83745

Observation 19340d35-d8dc-492b-a01e-da2c002f59f8 · outbound

This paper cites Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models

Reference 70

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source=pdf_text observed=2026-08-04T20:49:37.834886Z digest=sha256:d4d8ba2b111dda1ed406a0b69af36e2aaa114a5f4eb97b5dd4f021636570aa81

Observation e0747679-0ef8-45fc-8748-90c167363d09 · outbound

This paper cites Open-Source AI-Powered Optimization in Scalene: Advancing Python Performance Profiling with DeepSeek-R1 and LLaMA 3.2.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Open-Source AI-Powered Optimization in Scalene: Advancing Python Performance Profiling with DeepSeek-R1 and LLaMA 3.2

Reference 71

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.838235Z digest=sha256:78b2b7463ce39cafb2b0d49cee8ba4a5e45e164aaccb209347cb6abc68a2e191

Observation 962487d1-4a09-403b-9316-9085a2f737fb · outbound

This paper cites Reinforcement Learning Enhanced LLMs: A Survey.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Reinforcement Learning Enhanced LLMs: A Survey

Reference 72

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source=pdf_text observed=2026-08-04T20:49:37.841831Z digest=sha256:68c4514f94692782f4f387469a762d4c8f2dc8a986c3a0cb16b2acc5fda5edbb

Observation 686ddd5b-ecaa-4113-8836-f4563aff8576 · outbound

This paper cites an unresolved cited work.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Unresolved cited work

Reference 73

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source=pdf_text observed=2026-08-04T20:49:37.845804Z digest=sha256:75ededfa22d81d4b8284e882182a6bcebe981739badec492dcd96f7b4abc2ba8

Observation 3949d292-4765-4560-832c-495e536ca763 · outbound

This paper cites Critic-Guided Decoding for Controlled Text Generation.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Critic-Guided Decoding for Controlled Text Generation

Reference 74

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source=pdf_text observed=2026-08-04T20:49:37.849055Z digest=sha256:0f0de7522b1151cf9df6bbba8bfc5e07f1e5e644192a79dbc5c615d9118670d5

Observation a8ea7f94-81e9-4deb-96e4-6287b9e6be76 · outbound

This paper cites Reward modeling for mitigating toxicity in transformer- based language models.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Reward modeling for mitigating toxicity in transformer- based language models

Reference 75

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source=pdf_text observed=2026-08-04T20:49:37.852560Z digest=sha256:eea84d9797cb7c7fa3265d2e67b223e80f74c68becfb6be2d63e5c3e2cfba523

Observation 8b673578-115d-4b94-a7b6-5a32df5b2d83 · outbound

This paper cites Safe RLHF: Safe Reinforcement Learning from Human Feedback.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Safe RLHF: Safe Reinforcement Learning from Human Feedback

Reference 76

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source=pdf_text observed=2026-08-04T20:49:37.856497Z digest=sha256:75a4c38a81aa6ad0dc10b777993b7958f35e7fd2805e3a704ea24efbe6b04e72

Observation 5c83c194-5500-4e25-8fcd-2a01803b5c5d · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 77

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source=pdf_text observed=2026-08-04T20:49:37.860236Z digest=sha256:5af4628bcc5ee35c3b34d37dad36d13de6b2ecff79caa5e55e40f53d5eb53bc1

Observation c03c4cad-38a5-4fd7-b10b-54ea38f68ead · outbound

This paper cites CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

Reference 78

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source=pdf_text observed=2026-08-04T20:49:37.863607Z digest=sha256:ebf8342b80ff869ee4d3c9008fa92d34c0b41713d9ff1e165a1eeeabc472a849

Observation 390243b3-4aaf-49c1-a16e-221c9a50427c · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 79

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source=pdf_text observed=2026-08-04T20:49:37.867399Z digest=sha256:146d36397a9370d6ff697411a5d2c8a4311999f3955e2481916b5231cab4aebf

Observation 328e2211-4bb1-46a9-81e1-360362a1d399 · outbound

This paper cites Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents

Reference 80

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source=pdf_text observed=2026-08-04T20:49:37.871050Z digest=sha256:2326c9df4abcaafbdfa43042ccdf1a7bc0ff951168ef5f5f207cbc03f6bae357

Observation a0f5f510-9372-4477-8cdc-0e2fba197146 · outbound

This paper cites 2025.URL:https://openreview.net/forum? id=6y00rooi7i.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing 2025.URL:https://openreview.net/forum? id=6y00rooi7i

Reference 81

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raw_fallback, observed 2026-08-04T20:49:38.799899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.874812Z digest=sha256:f16d14bb4d2280261389bb9249851ec2d3d47f02c3936984cc5d1528e93fb572

Observation 7c42d6f5-becd-4159-bdfc-9ca99d52ee0a · outbound

This paper cites iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement

Reference 82

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source=pdf_text observed=2026-08-04T20:49:37.878308Z digest=sha256:fa9917fef4ab2039d63bb22d23a9cea9caf27115b153ca26025a4544e948e6fc

Observation 7cf13e67-2e61-4feb-8c4c-2dafb40dba46 · outbound

This paper cites Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxon- omy, and Methods.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxon- omy, and Methods

Reference 83

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arxiv_id, observed 2026-08-04T20:49:38.299793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.882153Z digest=sha256:aad17d75346378252bd6d2e63c39b01bbebd5c2c67922455444012ae8e1c7cdd

Observation 36150866-9739-4fc0-89e7-099a1d5f47f7 · outbound

This paper cites Learning by Reusing Previous Advice in Teacher- Student Paradigm.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Learning by Reusing Previous Advice in Teacher- Student Paradigm

Reference 84

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verified fuzzy
raw_fallback, observed 2026-08-04T20:49:38.789371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.885288Z digest=sha256:9809229cf7be48a3e251572c54aa54e3eeb093557cb7eafcf58280d48c90d4e1

Observation 04655b6d-2442-46b1-a37f-8cb5c2c79635 · outbound

This paper cites Minigrid & miniworld: modular & customizable reinforcement learning environments for goal-oriented tasks.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Minigrid & miniworld: modular & customizable reinforcement learning environments for goal-oriented tasks

Reference 85

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raw_fallback, observed 2026-08-04T20:49:38.778651Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.888834Z digest=sha256:288024ce0a3a87f613d60d93d87ba1a8d23538a0543182229eb4d2815bf1f00e

Observation 21d1212b-e624-4f07-9804-cbf5cdfcecbe · outbound

This paper cites MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Reference 86

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source=pdf_text observed=2026-08-04T20:49:37.891940Z digest=sha256:a9fafc943cc2c934a09b14fef26fdbe42f8bcef293977d930ecf35e4613b1d8f

Observation afd4d3b6-33c3-4d96-8c29-c5fe411cd001 · outbound

This paper cites Benchmarking the Spectrum of Agent Capabilities.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Benchmarking the Spectrum of Agent Capabilities

Reference 87

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source=pdf_text observed=2026-08-04T20:49:37.895334Z digest=sha256:7c3a3a8982770fc663b913f1d9ba78e7efcce4d2c99052ba8450035486276a4e

Observation 251ee647-48b4-4447-bdd0-a77c3aaaf30a · outbound

This paper cites LLM Augmented Hierarchical Agents.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing LLM Augmented Hierarchical Agents

Reference 88

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local_arxiv, observed 2026-08-04T20:49:38.262797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.898683Z digest=sha256:25fdfadfda4e081ec585ea6bb284f01a3baf6c12c53691f73bb11ec530fe6cdd

Observation b0ec105d-082d-40bb-bc08-ef0c9092a0f0 · outbound

This paper cites Teaching on a budget: agents advising agents in reinforcement learn- ing.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Teaching on a budget: agents advising agents in reinforcement learn- ing

Reference 89

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raw_fallback, observed 2026-08-04T20:49:38.766821Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.902315Z digest=sha256:bd1159e97c6cba8c28f7cbbacc865adc8db92b6360acb023f6069715ea9483f3

Observation 2db3009c-2e21-4587-a067-103389989162 · outbound

This paper cites Simultaneously Learning and Advising in Multiagent Reinforcement Learning.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Simultaneously Learning and Advising in Multiagent Reinforcement Learning

Reference 90

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.905768Z digest=sha256:3d70cbb13c81ad4d668a8c77d27c45df23856b96889138686b85cac2247f7b01

Observation 4708b46a-adca-41e8-a124-064c92278cf0 · outbound

This paper cites Multitask Prompted Training Enables Zero-Shot Task Generalization.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Multitask Prompted Training Enables Zero-Shot Task Generalization

Reference 91

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source=pdf_text observed=2026-08-04T20:49:37.908962Z digest=sha256:41f4986df056eab9e2bb96b6f3500e65694f6c5f49f3c118dd4a66110a9a77af

Observation df71067c-76a5-43fe-bd15-57f36c7c681f · outbound

This paper cites Language Models are Few-Shot Learners.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Language Models are Few-Shot Learners

Reference 92

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source=pdf_text observed=2026-08-04T20:49:37.912725Z digest=sha256:1d3d6e6c2a43bb4c6adc23803a9512ba913b79ee487658a71899bf24b7c5ca87

Observation 9ceff9e6-c61b-4420-a61f-2df51d321fd0 · outbound

This paper cites Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

Reference 93

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source=pdf_text observed=2026-08-04T20:49:37.916750Z digest=sha256:514689170dbdfbb4d09a1aef202b5b07c369fffa308d210df43e01dfbc5f731e

Observation c388a109-cd3e-4377-9484-3db35a08d0b8 · outbound

This paper cites an unresolved cited work.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Unresolved cited work

Reference 94

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.920386Z digest=sha256:bbcc7f3d646262457f3dc4c6e3750ddefa8260589f8124117cb7335ed3cc0d1d

Observation 3fbc8c99-6316-40d1-a694-bf2e9412fe14 · outbound

This paper cites Using Ollama.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Using Ollama

Reference 95

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raw_fallback, observed 2026-08-04T20:49:38.734303Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.927393Z digest=sha256:cb18eaf74ba32b0ae42b236e5674ec829afbd3ce26fba88771d5eb97f9fa6e88

Observation 8a031c54-11f9-43b3-9d6b-f8de9eb01719 · outbound

This paper cites SPRIG: Improving Large Language Model Performance by System Prompt Optimization.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing SPRIG: Improving Large Language Model Performance by System Prompt Optimization

Reference 96

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source=pdf_text observed=2026-08-04T20:49:37.930680Z digest=sha256:9aa2f02d4f18a0d747d8276e44a9da3a1ba36c152bd90efaa5d087ea57931cfe

Observation dc7b8055-0f57-4de5-ac93-93d04dc9001c · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 97

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source=pdf_text observed=2026-08-04T20:49:37.933858Z digest=sha256:38aaf1038036e8a79c8f4985175b7c26c3c46e3ad9614bbeefb04f009cf51944

Observation b19d0852-b7a0-48af-b8f5-a59931772fc7 · outbound

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

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing ReAct: Synergizing Reasoning and Acting in Language Models

Reference 98

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:49:37.937179Z digest=sha256:cc907fb6cccd29e38387eec4e5da9068791a250b8159e45ff261e3db7e559af4

Observation 7d6491d8-6382-46ce-8df1-de2c91c42dc9 · outbound

This paper cites On the Partitioning of GPU Power among Multi-Instances.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing On the Partitioning of GPU Power among Multi-Instances

Reference 99

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local_arxiv, observed 2026-08-04T20:49:38.182311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.940585Z digest=sha256:7756079b6aef7b494597931be3ddef499f6824b5fb8b929fc467765e85f2d0b3

Observation 9ca0dc91-adc6-4ea1-84c7-35e27eeb421f · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 100

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:49:37.943759Z digest=sha256:12d50ab3756e578acfb473b53e08e6dc69f17c575c0838039f3f5661e78efcd9

Observation 839ed5fb-3ec6-4362-8bc4-6d1680a95ca0 · outbound

This paper cites Local Large Language Models for Complex Structured Medical Tasks.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Local Large Language Models for Complex Structured Medical Tasks

Reference 101

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local_arxiv, observed 2026-08-04T20:49:38.157716Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.947200Z digest=sha256:ebf09bc7fb640803e9562b8963df0d587d015073d77867efecddb888e4edef18

Observation fb9bfbc1-5c07-4cf1-964a-569affae9c8b · outbound

This paper cites Comprehensive testing of large language models for extraction of structured data in pathology.

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing Comprehensive testing of large language models for extraction of structured data in pathology

Reference 102

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malformed identifier
doi_truncated, observed 2026-08-04T20:49:38.055904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-04T20:49:37.950968Z digest=sha256:ec1eb0d1294c8c94636f7ce7064c653fed5ae7ea22f2734d39f465871d622a05

Pith citing papers

No inbound Pith citation observations are available.