Paper Citation Record · LEDGER

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

As of 8 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 12 inbound Pith citation observations for arXiv:2507.02841.

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

pith.paper-citation-record.v1
2507.02841 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:26:48.296848Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:53:10.631113Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:00:08.075019Z

Reference resolution

28 of 28 outbound references displayed

verified exact0
verified fuzzy1
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 53902c7f-ceaa-4cd8-ae04-df45ca4878b2 · outbound

This paper cites Concrete Problems in AI Safety.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Concrete Problems in AI Safety

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:45.550459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 657f7dfc-fbbc-40ff-9958-12be451463f3 · outbound

This paper cites SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:45.878265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93a0d590-15ee-4f88-9130-d17931502e63 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:46.011935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5086abbf-6a0c-47bf-89e1-13bc93dfb8a8 · outbound

This paper cites DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:46.372789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 806c79e2-5a96-4689-90ea-3f25cfc6655e · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Measuring Mathematical Problem Solving With the MATH Dataset

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:46.458931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b383e2a9-380d-49a0-bf16-fe6abb213d44 · outbound

This paper cites Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:46.553671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 200c78f7-87fc-4304-90aa-fd1b3003eee1 · outbound

This paper cites OpenAI o1 System Card.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason OpenAI o1 System Card

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:46.651273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ac67542-5f34-49c7-b98a-53504476c0d9 · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Understanding R1-Zero-Like Training: A Critical Perspective

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:46.745830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1e0f7d7-0436-40f6-9d38-34d50fec9880 · outbound

This paper cites The Climb Carves Wisdom Deeper Than the Summit: On the Noisy Rewards in Learning to Reason.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason The Climb Carves Wisdom Deeper Than the Summit: On the Noisy Rewards in Learning to Reason

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:46.857127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7aaf876e-1be8-4460-aac9-ebda4c7a5c30 · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Asynchronous methods for deep reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:26:48.607497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1f6ef865-02fe-46fb-a6e1-f9f437debb17 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:47.156279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70addeb1-6245-49eb-9b54-4269a32733bd · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason HybridFlow: A Flexible and Efficient RLHF Framework

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:47.348775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93f8688a-5a95-4f2c-8ade-b482b719f4a2 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:47.488611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fcf59b5-b3a2-4d95-ab77-06c710e09d8c · outbound

This paper cites Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:47.740169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a7f977f-f13f-4f48-a2e8-ee4c534fe2b4 · outbound

This paper cites Learning to Reason under Off-Policy Guidance.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Learning to Reason under Off-Policy Guidance

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:47.836925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 188137c9-c0d6-41d2-bfc0-301584891c98 · outbound

This paper cites Qwen2 Technical Report.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Qwen2 Technical Report

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:47.918660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3148fa1c-30e0-4b98-acfb-40c08c2a5224 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:48.035446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1551ff57-ec59-4a4b-b813-29883e73e219 · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:48.085634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59db289d-0fdf-468d-b436-3c737347f318 · outbound

This paper cites SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:48.151553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23ad57c4-f146-45a3-9a69-58be11b90cb3 · outbound

This paper cites an unresolved cited work.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:26:48.597951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 26de7637-ffdd-4a6c-869b-47c3972b6a63 · outbound

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

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 1948

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:47.242484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c032893-f8dc-4e88-9ad2-bbd2e931f2cd · outbound

This paper cites Orca-Math: Unlocking the potential of SLMs in Grade School Math.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Orca-Math: Unlocking the potential of SLMs in Grade School Math

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:46.985350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42f467dd-ab2f-4c2b-8753-a8a7af1b99b5 · outbound

This paper cites SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:45.633252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b04e2f4d-a1b8-467c-8b58-6f70f84d6524 · outbound

This paper cites Reasoning with Exploration: An Entropy Perspective.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Reasoning with Exploration: An Entropy Perspective

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:45.833877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91e9def5-e837-48b2-b972-09dd171b7349 · outbound

This paper cites MathPile: A Billion-Token-Scale Pretraining Corpus for Math.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason MathPile: A Billion-Token-Scale Pretraining Corpus for Math

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:47.613196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06f84943-4e26-4301-82be-f15ef631ab84 · outbound

This paper cites The Llama 3 Herd of Models.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason The Llama 3 Herd of Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:46.134022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f09d8f1-7c70-4020-8387-322b242215ba · outbound

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

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:46.255880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98de604d-3fd3-4c47-9df0-62ca1eb1dfdc · outbound

This paper cites Evaluating Large Language Models Trained on Code.

StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason Evaluating Large Language Models Trained on Code

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:45.719770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 63ed6416-f211-42d3-8ae8-6792164f8643 · inbound

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance cites this paper.

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T10:53:10.631113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5a95a1a-5436-40a1-9875-db81b3a9ba99 · inbound

Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime cites this paper.

Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:55:42.926347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation da54ada4-7c1f-4b5b-8944-c9de425040ab · inbound

Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime cites this paper.

Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:55:54.508842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation da2a00c6-4e88-4cb1-998e-660119840990 · inbound

Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime cites this paper.

Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T17:32:41.453150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 50102c52-6333-46ce-867d-49c934cbcc78 · inbound

Seir\^enes: Adversarial Self-Play with Evolving Distractions for LLM Reasoning cites this paper.

Seir\^enes: Adversarial Self-Play with Evolving Distractions for LLM Reasoning StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:22:01.670232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 48091707-a566-4411-ab25-00bddaffb930 · inbound

DelTA: Discriminative Token Credit Assignment for Reinforcement Learning from Verifiable Rewards cites this paper.

DelTA: Discriminative Token Credit Assignment for Reinforcement Learning from Verifiable Rewards StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T05:29:40.154298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 604f0662-faf6-49e7-87a6-41b31def1ce5 · inbound

LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance cites this paper.

LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:21:10.335384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 102dbb83-68ec-464a-a929-a3df65e0e30e · inbound

Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning cites this paper.

Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:28:33.912680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5093c845-1be9-4bd5-a621-1039b93d1e57 · inbound

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients cites this paper.

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:56.192377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 49501d2a-a3eb-4771-bbb5-a115b9e2e2b4 · inbound

The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms cites this paper.

The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:00:08.076804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e162964d-6e0c-4a53-96a2-b7f75a8d13c6 · inbound

The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms cites this paper.

The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:09:50.539506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7b0118de-0224-4485-af04-c1dad64f514f · inbound

Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Information cites this paper.

Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Information StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-01T12:54:36.127455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.