Paper Citation Record · LEDGER

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective

As of 7 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 3 inbound Pith citation observations for arXiv:2506.02553.

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

pith.paper-citation-record.v1
2506.02553 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:32:31.776781Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:07:30.092931Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T08:40:41.514103Z

Reference resolution

43 of 43 outbound references displayed

verified exact1
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External citation measurements

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Outbound references

Observation a1610d62-8496-4528-b307-6534336b9c74 · outbound

This paper cites GPT-4o System Card.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective GPT-4o System Card

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:28.119855Z

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Observation 073987cd-41cf-4941-a263-ca7b0bcbdc3c · outbound

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

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Gemini: A Family of Highly Capable Multimodal Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:28.178328Z

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Observation d847781b-9233-4555-8232-c1e126abbad6 · outbound

This paper cites Qwen2.5 Technical Report.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Qwen2.5 Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:28.272952Z

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Observation fac76498-5a66-438f-8048-394162622066 · outbound

This paper cites The amazon nova family of models: Technical report and model card.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective The amazon nova family of models: Technical report and model card

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:33.438977Z

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Observation 8cfe9e74-aa5e-4339-a953-161932231c2a · outbound

This paper cites OpenAI o1 System Card.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective OpenAI o1 System Card

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:28.487274Z

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Observation 838fd653-fbef-4b82-b5c1-7cc2b920e801 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:28.616012Z

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Observation 206e65fa-4c05-4e23-83ff-e6b70f85acf8 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Proximal Policy Optimization Algorithms

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:28.675759Z

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Observation 7a22d8ee-ed50-44dc-9392-7f7f9866d0bb · outbound

This paper cites Learning to summarize with human feedback.Advances in neural information processing systems, 33:3008–3021, 2020.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Learning to summarize with human feedback.Advances in neural information processing systems, 33:3008–3021, 2020

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:28.759565Z

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Observation db41d348-05c2-4c17-b586-f1c328306197 · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:28.849369Z

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Observation fa568fdd-33ba-47a7-8f3a-c6a07f2bdd31 · outbound

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

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:28.936948Z

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Observation b3b842df-d41e-4957-991c-f0ea6963c5f2 · outbound

This paper cites ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:29.011230Z

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Observation 92d7bb58-110a-4dc1-b1a8-d6fdf26249d2 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:29.157791Z

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Observation ac4235ba-1686-4383-89d7-670ef940fb6a · outbound

This paper cites Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:29.285863Z

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Observation c31fd329-18ae-4c4a-af35-5ef3063ac485 · outbound

This paper cites DPO Meets PPO: Reinforced Token Optimization for RLHF.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective DPO Meets PPO: Reinforced Token Optimization for RLHF

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:29.379167Z

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Observation 3d4739b4-8fbb-4f16-9d85-ca8afd503afd · outbound

This paper cites Dense Reward for Free in Reinforcement Learning from Human Feedback.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Dense Reward for Free in Reinforcement Learning from Human Feedback

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:29.474618Z

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Observation 9dc7d6f1-aa41-45d1-a570-87e7f284de1d · outbound

This paper cites Process Reinforcement through Implicit Rewards.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Process Reinforcement through Implicit Rewards

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:29.517440Z

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Observation d1e21099-45e5-456f-9a22-a89f33de763d · outbound

This paper cites RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T11:32:32.289654Z

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Observation 226e8b7d-71ea-4786-aa9b-ea8cb8881666 · outbound

This paper cites Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:29.652497Z

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Observation 0f06674a-aab8-4c2f-ace5-3d413506452c · outbound

This paper cites Reinforcement learning: An introduction.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Reinforcement learning: An introduction

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:33.106505Z

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Observation e3eae744-e475-416a-b86c-3b11a18d485e · outbound

This paper cites Analysis of on-policy policy gradient methods under the distribution mismatch.arXiv preprint arXiv:2503.22244, 2025.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Analysis of on-policy policy gradient methods under the distribution mismatch.arXiv preprint arXiv:2503.22244, 2025

Reference 20

Resolution: verified exact; raw_fallback, observed 2026-08-07T11:32:32.175493Z

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Observation ebc8704f-7ad7-42cb-b105-420a41cba4df · outbound

This paper cites High-dimensional continu- ous control using generalized advantage estimation, 2018.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective High-dimensional continu- ous control using generalized advantage estimation, 2018

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:32.986611Z

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

Observation 229e0672-ac94-41c0-aad3-7d737231e735 · outbound

This paper cites VinePPO: Refining Credit Assignment in RL Training of LLMs.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective VinePPO: Refining Credit Assignment in RL Training of LLMs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:30.026404Z

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Observation faea92d1-0529-408c-8cf3-62950293087a · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Gonzalez, Hao Zhang, and Ion Stoica

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:30.149593Z

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Observation 8d855c2e-f274-4eab-97d1-d23f6041ab13 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36:53728–53741, 2023.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36:53728–53741, 2023

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:32.869318Z

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

Observation 3d37a5d8-7487-4031-94a2-c63c0b08b245 · outbound

This paper cites SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:30.314881Z

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Observation 06c03cc1-5efd-4109-ad8b-30f0011fe3dc · outbound

This paper cites Multi-turn Reinforcement Learning from Preference Human Feedback.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Multi-turn Reinforcement Learning from Preference Human Feedback

Reference 26

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Observation 4ef9d1d0-0259-4636-8775-80a7ff65f737 · outbound

This paper cites LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:30.490860Z

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Observation f04a9702-4b69-44c0-ac3b-86915a75c762 · outbound

This paper cites ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:30.549942Z

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Observation 40d8a2c3-061f-4d2d-ab59-133b144a810e · outbound

This paper cites Offline Reinforcement Learning for LLM Multi-Step Reasoning.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Offline Reinforcement Learning for LLM Multi-Step Reasoning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:30.615635Z

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Observation 46c7a99b-9411-4c48-9a88-2d440dd9bf5d · outbound

This paper cites PlanGenLLMs: A Modern Survey of LLM Planning Capabilities.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective PlanGenLLMs: A Modern Survey of LLM Planning Capabilities

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:30.682383Z

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Observation d316c10c-5751-4cb2-a82f-c5351ec05453 · outbound

This paper cites MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:30.761569Z

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Observation 4c96c6b6-cb23-4153-9448-1ad05c0b0ae9 · outbound

This paper cites Interactive evaluation for medical LLMs via task- oriented dialogue system.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Interactive evaluation for medical LLMs via task- oriented dialogue system

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:32.721910Z

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Observation 7ed50a14-fea9-4537-9fe4-15c531531cc2 · outbound

This paper cites Let’s verify step by step, 2023.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Let’s verify step by step, 2023

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:30.927433Z

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Observation 7a0a2145-8133-4678-ac20-be71fc5c50ae · outbound

This paper cites Unraveling rlhf and its variants: Progress and practical engineering insights.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Unraveling rlhf and its variants: Progress and practical engineering insights

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:32:32.554729Z

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Observation c4d243be-85b3-4deb-8dd7-850e0b14cfb7 · outbound

This paper cites Solving math word problems with process- and outcome-based feedback.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Solving math word problems with process- and outcome-based feedback

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:31.151918Z

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Observation ce13e47a-3bf7-42d3-bbf8-efa2c0227fb7 · outbound

This paper cites Fine-grained human feedback gives better rewards for language model training.Advances in Neural Information Processing Systems, 36:59008–59033, 2023.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Fine-grained human feedback gives better rewards for language model training.Advances in Neural Information Processing Systems, 36:59008–59033, 2023

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:31.223415Z

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Observation 86e6e972-8ff4-4c20-a920-2dfcddb4ecf9 · outbound

This paper cites Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:31.310922Z

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Observation 70da07b1-0cf1-44f1-ba5c-0c3613c1b6ab · outbound

This paper cites TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:31.365581Z

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Observation 005c73bf-7ead-4a34-a89f-163671031cb4 · outbound

This paper cites Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:31.431529Z

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Observation 867e3e6f-9b30-47c1-b77b-cd076a11bcef · outbound

This paper cites Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:31.527587Z

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Observation eb286a80-c534-4ce2-98f5-35563562af88 · outbound

This paper cites Chatbot arena: An open platform for evaluating llms by human preference.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Chatbot arena: An open platform for evaluating llms by human preference

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:31.601560Z

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Observation b472d78c-92ea-4f16-8e06-1c1a56c278ee · outbound

This paper cites Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:31.693251Z

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Observation 71bc59d4-7290-4a87-b902-305f8be53e54 · outbound

This paper cites MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions.

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T11:32:31.776781Z

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Pith citing papers

Observation 458b6a9e-4f12-408c-853b-786e6f2ba5ed · inbound

Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models cites this paper.

Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective

Reference 252

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:40:41.517098Z

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Observation cff38d40-0384-4dfd-b50f-4b7a1be48da5 · inbound

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle cites this paper.

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T16:07:30.092931Z

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Observation 389afa80-1000-430a-9628-73a046081c5c · inbound

Relative Score Policy Optimization for Diffusion Language Models cites this paper.

Relative Score Policy Optimization for Diffusion Language Models Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:56:29.690474Z

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