Paper Citation Record · LEDGER

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

As of 10 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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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.