mlsanityData to 7 Oct 2026

HelpSteer3: Human-Annotated Feedback and Edit Data to Empower Inference-Time Scaling in Open-Ended General-Domain Tasks

arXiv 2503.04378 ↗First posted 6 Mar 2025cs.CL cs.AI cs.LGcited by 16

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

Inference-Time Scaling has been critical to the success of recent models such as OpenAI o1 and DeepSeek R1. However, many techniques used to train models for inference-time scaling require tasks to have answers that can be verified, limiting their application to domains such as math, coding and logical reasoning. We take inspiration from how humans make first attempts, ask for detailed feedback from others and make improvements based on such feedback across a wide spectrum of open-ended endeavors. To this end, we collect HelpSteer3 data to train dedicated Feedback and Edit Models that are capable of performing inference-time scaling for open-ended general-domain tasks. In our setup, one model generates an initial response, which are given feedback by a second model, that are then used by a third model to edit the response. We show that performance on Arena Hard, a benchmark strongly predictive of Chatbot Arena Elo can be boosted by scaling the number of initial response drafts, effective feedback and edited responses. When scaled optimally, our setup based on 70B models from the Llama 3 family can reach SoTA performance on Arena Hard at 92.7 as of 5 Mar 2025, surpassing OpenAI o1-preview-2024-09-12 with 90.4 and DeepSeek R1 with 92.3.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 50 of its 76 references that are in the map. First posted on arXiv 6 Mar 2025.

Builds on

50 of 76 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024O1 Replication Journey: A Strategic Progress Report -- Part 18 Oct
  2. 2024Small Language Models Need Strong Verifiers to Self-Correct Reasoning26 Apr
  3. 2024CriticBench: Benchmarking LLMs for Critique-Correct Reasoning22 Feb
  4. 2023LLMs cannot find reasoning errors, but can correct them given the error location14 Nov

Other references · 46

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  5. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  7. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  8. 2023Let's Verify Step by Stepcited by 3,598
  9. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,928
  10. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,883
  11. 2024Mixtral of Expertscited by 1,785
  12. 2017Decoupled Weight Decay Regularizationcited by 1,722
34 more
  1. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,151
  2. 2024Tulu 3: Pushing Frontiers in Open Language Model Post-Trainingcited by 988
  3. 2023Teaching Large Language Models to Self-Debugcited by 963
  4. 2024SimPO: Simple Preference Optimization with a Reference-Free Rewardcited by 951
  5. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 889
  6. 2024Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluatorscited by 865
  7. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  8. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 712
  9. 2024Self-Rewarding Language Modelscited by 654
  10. 2024WildChat: 1M ChatGPT Interaction Logs in the Wildcited by 598
  11. 2024Generative Verifiers: Reward Modeling as Next-Token Predictioncited by 465
  12. 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Trainingcited by 399
  13. 2023UltraFeedback: Boosting Language Models with Scaled AI Feedbackcited by 394
  14. 2022Self-critiquing models for assisting human evaluatorscited by 372
  15. 2024Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researcherscited by 348
  16. 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Searchcited by 338
  17. 2022Generating Sequences by Learning to Self-Correctcited by 271
  18. 2024RLHF Workflow: From Reward Modeling to Online RLHFcited by 265
  19. 2024HelpSteer2: Open-source dataset for training top-performing reward modelscited by 232
  20. 2024Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judgecited by 202
  21. 2024RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learningcited by 146
  22. 2024Nemotron-4 340B Technical Reportcited by 143
  23. 2024HelpSteer2-Preference: Complementing Ratings with Preferencescited by 129
  24. 2024Granite Code Models: A Family of Open Foundation Models for Code Intelligencecited by 116
  25. 2024Critique-out-Loud Reward Modelscited by 105
  26. 2024Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMscited by 103
  27. 2024Aligner: Efficient Alignment by Learning to Correctcited by 96
  28. 2023Improving Code Generation by Training with Natural Language Feedbackcited by 75
  29. 2024Can Language Models Solve Olympiad Programming?cited by 70
  30. 2024Self-Generated Critiques Boost Reward Modeling for Language Modelscited by 69
  31. 2024Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHFcited by 57
  32. 2024Improving Reward Models with Synthetic Critiquescited by 44
  33. 2024NeMo-Aligner: Scalable Toolkit for Efficient Model Alignmentcited by 40
  34. 2024Learning to Refine with Fine-Grained Natural Language Feedbackcited by 27

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 7.

16 papers.

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  1. 2025Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergycited by 204
  2. 2025Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Modelscited by 33
  3. 2025Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Modelscited by 15
  4. 2025DuPO: Enabling Reliable LLM Self-Verification via Dual Preference Optimizationcited by 7
  5. 2025RLBFF: Binary Flexible Feedback to bridge between Human Feedback & Verifiable Rewardscited by 7
  6. 2025Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domainscited by 5
  7. 2025Knowledge-Level Consistency Reinforcement Learning: Dual-Fact Alignment for Long-Form Factualitycited by 3
  8. 2025Towards Faithful and Controllable Personalization via Critique-Post-Edit Reinforcement Learningcited by 3
  9. 2026C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferencescited by 3
  10. 2026Mitigating Translationese Bias in Multilingual LLM-as-a-Judge via Disentangled Information Bottleneckcited by 2
  11. 2026RoPoLL: Robust Panel of LLM Judgescited by 2
  12. 2026Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generationcited by 1
4 more
  1. 2026Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMscited by 1
  2. 2025Unsupervised Evaluation of Multi-Turn Objective-Driven Interactionscited by 0
  3. 2026CARE-RL: Capability-Aware Reinforcement Learning for Mitigating Cross-Domain Conflictscited by 0
  4. 2026OSWorld-Pro: Process-based Evaluation for Computer Use Agentscited by 0

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  2. 2025Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners
  3. 2025Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking
  4. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
  5. 2025Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals
  6. 2025Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
  7. 2025Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering
  8. 2026Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility