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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 21 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

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measured 100 of 113 reference resolution

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measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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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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Observation 34f310e9-2063-451b-a77b-437c94e076ab · outbound

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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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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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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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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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

Reference 23

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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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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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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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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 A reinforcement learning approach to obtain treatment strategies in sequential medi- cal decision problems

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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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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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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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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:e20c9142020edf0940b179ca8c2cb6335e5251452b50c1e8975adb87d1521269

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:dc938c3c709ed587f4222b62c83b4f99948c467118ee1e950b1a3ce7d81498bb

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:af4b6a843a9ef9dd68197892a79584831215c1b71e0472046cefc1c8ebb92896

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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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=sha256:520510f06e3848ec0a47e87f313aae2d802ba2e62404d3050a6cffae5f165f8a

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:a7bce1d468f04e5be53e2e110ef3a2c0c09c44be54d92a2951ce6e35a461d4cb

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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source=pdf_text observed=2026-08-04T20:49:37.766956Z digest=sha256:346f68b058a7b441345889074d3f5b83d4e204288bcc67f584f1acc125191c41

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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source=pdf_text observed=2026-08-04T20:49:37.770353Z digest=sha256:0ff4dfd3a78619a1a9974ed858fd509f20e18359c87b5250b84988248a95b6d5

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=sha256:314123ef6d565c3054b5487d20eaa0c215f06ba59cbeca044d6a48cd829a9f46

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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source=pdf_text observed=2026-08-04T20:49:37.776970Z digest=sha256:c064abaa3eb5ff1a77e903c6d1369575c527ab88d6fa7a03e130a9efb223fe0b

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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source=pdf_text observed=2026-08-04T20:49:37.780199Z digest=sha256:7ebeaa47e7f444d1bcfff617da92432f1b70b2c79db339a1cc7aa8c8598312d3

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=sha256:417cd9e61a7676b0918be5c16a4b63547c7bd2d89f7c05f5958d3a5ab0cac20b

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=sha256:7e28d5c06b3bd75f1f0ee433df1481897556d140ad703639269f921277283bc5

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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source=pdf_text observed=2026-08-04T20:49:37.800923Z digest=sha256:d9efb589fa82751b2619e0456ac0c4ebacf4d8d56593e770c805db2114aa2fe2

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=sha256:5a804e79ff2861f536eae2949108acc0f3d9babf9b112f5bef8451d0d1191748

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=sha256:7d0f8bc9aa8e0134c6977130e5c87ff7c7a457dab6ec8f89e6b9cea8657a148c

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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source=pdf_text observed=2026-08-04T20:49:37.815064Z digest=sha256:60a92f652d3d46d57fe32235ac37bb111186fa73a34dc14562eb801241e2a671

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:df1cd3fe94b42057c2f594a93f197d9edf2e479f98f85a300b124d79031395b1

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:a6a5ed62cf756a6f95734bfba456aab7439813e560a96990a2f8d7b7d4edea78

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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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.825035Z digest=sha256:8f2fe59a4d7b35a366dc998665c2f7123ea1f0dec869b55993fa7754dec778b4

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=sha256:58072a24213bcc811e9b3037d7afec5291e0894e2ada4437de708fed33c135d0

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=sha256:1b7b28b8258c5aac91c94a6fac4624faf036f6522c8ac04f9c3115a0ab8035c0

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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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.834886Z digest=sha256:1e1a12cbe55f63f2b7ea40395f214779a41a4fc8a80f2f70b90b8778c5cec8f7

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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source=pdf_text observed=2026-08-04T20:49:37.838235Z digest=sha256:0da68d2bf3d38a2827bba15e37c363b984da702b764714f8f262605b1c114ebb

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:f750484b93e8fb8e5cf7774d3354b65dbff410665661dbef8c31b44a04bda23a

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=sha256:07ff550a7a6139e09fb90daf123b32ad4407bf690089a14c780dac09550fcbe0

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=sha256:3a86f1df8161cbeecc8fef00ba720dcca5d9b91768cdb585cb3ed40b8cd26fa0

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=sha256:2f3979a0a816bd477ebc940fc3201bc563565d08d43a278321b98dd37b1267ef

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:f444937ec5550d0b5ad4a81f2aface3af297aa6ed17c277a7218560bbec01148

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=sha256:3b4c0e1e702156a794b92a2ff8ec73a14be82eb9719d44b007886ce25982299a

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=sha256:96178f917b4439347173645613ae1a7ed00504bd28627673849469e30c42041a

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=sha256:1d9a45aeba72d50b4201874016abbd885e7f5ab7be85c15a334e41652ff393d0

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:cedc34156d2a82a44ecc7bfc72c6eaf54035dc9a2c38966d59b3de300e605b73

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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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.874812Z digest=sha256:683df88f95247bdd8db5d560edcf50fc737a372f89e6f056cb5d343b5cee1232

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=sha256:08f4743a911f9b0faf5af8644ebfab18400c2bf0e4f3b974bb432f436c1fe3d7

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.882153Z digest=sha256:d838ac42d5e0eb94b55882d7a4dd31d0f59572e01516f00e039b1b83afb897f1

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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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.885288Z digest=sha256:adf19fbbf76df16925011784c69e8aaecd04df3c9f221637af11a056ec837785

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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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.888834Z digest=sha256:8e85bbdf9031dd034c096d323ec4a2dc622df7ec7c08539b95afd7501a3156e8

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:d86579a8c4f5b18de96ef448261d0b688df7b7d0d26e403bb2eaa827c9e0e449

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=sha256:18770e227b4b51b9ab981d035c2e77622c041b376cd5770c0948377db2af8065

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.898683Z digest=sha256:90ae123bda81f3b2e64d5be7255ee96f77356b9389f24e4a28b4120eafe36daf

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.902315Z digest=sha256:4ae467e5eda07e5feee46de657861761199b98c3013e70ac3bca4deac9d2f0f5

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.905768Z digest=sha256:e76f9d449448a613adf7a61bb46b29be1d703dddf6f94578564950e0ba656fff

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=sha256:9fa02b81fc419b86ec0a37efbb3939fba31724dd4d4b0de0db1fdfaf7d6ae5e8

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=sha256:33fae937c4d7e9e8dfce5de131d007ecb71e845bbbf4f02f60b960e2cdc9477d

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=sha256:2fb2ef83349d33139beffd1d425503de9eee7b0df157c4638c660673c084a6ec

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.920386Z digest=sha256:e86bf9d94405c97a9ced0f5f3a1faa62aa0efc44068ddd7d612cbcfad8420632

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.927393Z digest=sha256:a11ce2cb57b806e2a6178ed27c59a417d39a9c76dcfc51821e8b6649591f7191

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:ed3f0d472b2a5ab0b3ae819438cd8217a8304c24a6ad682447b473d2bc100e01

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:a56307b5430ba98a2024baedc5555abc862b567414e251f9406d1a94b1ddc90c

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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source=pdf_text observed=2026-08-04T20:49:37.937179Z digest=sha256:aebea204f220edb31a9874a94995d4e14e7b859b550f656d8d2e7f048938475f

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.940585Z digest=sha256:9d47eeb6a4ddc1a90dc8beb0daa9a2092ee8f38416f8c738fa17197298b79bbc

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=sha256:1c27bbd8e7b97af700c3548183b8535fdb038cc2c86f341eace0e38f83ec1fab

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.947200Z digest=sha256:4d8ffdd39882e9985f6a81b3522c76c397fc134416e6ffe621a8aa5590eaa568

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-04T20:49:37.950968Z digest=sha256:aa8d4a23371faec789d652b32c9756a877c5f8460476aa994b398434a67f32ea

Pith citing papers

No inbound Pith citation observations are available.