Pith. sign in

REVIEW 20 cited by

O1 Replication Journey: A Strategic Progress Report -- Part 1

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.18982 v1 pith:FHY6ZA24 submitted 2024-10-08 cs.AI cs.CL

O1 Replication Journey: A Strategic Progress Report -- Part 1

classification cs.AI cs.CL
keywords researchjourneyincludingexplorationlearningprocessreplicationprogress
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey. In response to the announcement of OpenAI's groundbreaking O1 model, we embark on a transparent, real-time exploration to replicate its capabilities while reimagining the process of conducting and communicating AI research. Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects, delayed information sharing, and the lack of recognition for diverse contributions. By providing comprehensive, real-time documentation of our replication efforts, including both successes and failures, we aim to foster open science, accelerate collective advancement, and lay the groundwork for AI-driven scientific discovery. Our research progress report diverges significantly from traditional research papers, offering continuous updates, full process transparency, and active community engagement throughout the research journey. Technologically, we proposed the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process, including trial and error, reflection, and backtracking. With only 327 training samples and without any additional tricks, journey learning outperformed conventional supervised learning by over 8\% on the MATH dataset, demonstrating its extremely powerful potential. We believe this to be the most crucial component of O1 technology that we have successfully decoded. We share valuable resources including technical hypotheses and insights, cognitive exploration maps, custom-developed tools, etc at https://github.com/GAIR-NLP/O1-Journey.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 20 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Answer-Conditioned Chains of Thought Degrade Verifiable-Reasoning Distillation in Large Language Models

    cs.CL 2026-07 conditional novelty 6.0

    Answer-conditioned chains of thought are harmful supervision for reasoning distillation—causing up to ~27-point accuracy drops—and the damage is invisible to correctness filters.

  2. MADE: Beyond Scoring via a Multilingual Agentic Diagnosing Engine for Fine-Grained Evaluation Insights

    cs.CL 2026-06 unverdicted novelty 6.0

    MADE is a new multilingual agentic diagnosing engine that produces higher-quality diagnostic reports (47% better than baseline) on a large-scale evaluation substrate covering 33 model families and 26 languages.

  3. AIPO: Learning to Reason from Active Interaction

    cs.CL 2026-05 unverdicted novelty 6.0

    AIPO adds active multi-agent consultation (Verify, Knowledge, Reasoning agents) plus custom importance sampling to RLVR training so LLMs expand their reasoning boundary and then operate without the agents.

  4. AIPO: Learning to Reason from Active Interaction

    cs.CL 2026-05 unverdicted novelty 6.0

    AIPO trains LLMs to expand their reasoning capability boundary via active multi-agent interaction with Verify, Knowledge, and Reasoning agents during RLVR, using importance sampling and clipping to handle feedback, th...

  5. LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection

    cs.CL 2025-09 conditional novelty 6.0

    LAMDAS selects domain-relevant training data via an LLM likelihood ratio with a learned domain prefix, beating full-data training and nine baselines on code and math.

  6. The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

    cs.AI 2025-09 accept novelty 6.0

    Survey that defines agentic RL for LLMs via POMDPs, introduces a taxonomy of planning/tool-use/memory/reasoning capabilities and domains, and compiles open environments from over 500 papers.

  7. CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning

    cs.CL 2025-07 unverdicted novelty 6.0

    CoLD mitigates length bias in process reward models for mathematical reasoning via counterfactual guidance, length penalties, bias estimation, and joint training, improving step selection accuracy and conciseness on M...

  8. WebThinker: Empowering Large Reasoning Models with Deep Research Capability

    cs.CL 2025-04 unverdicted novelty 6.0

    WebThinker equips large reasoning models with autonomous web exploration and interleaved reasoning-drafting via a Deep Web Explorer and RL-based DPO training, yielding gains on GPQA, GAIA, and report-generation benchmarks.

  9. ToolRL: Reward is All Tool Learning Needs

    cs.LG 2025-04 conditional novelty 6.0

    A principled reward design for tool selection and application in RL-trained LLMs delivers 17% gains over base models and 15% over SFT across benchmarks.

  10. OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles

    cs.CV 2025-03 conditional novelty 6.0

    Iterative SFT-RL cycles enable a 7B LVLM to develop sophisticated visual chain-of-thought reasoning and improve performance on math and general reasoning benchmarks.

  11. LIMO: Less is More for Reasoning

    cs.CL 2025-02 unverdicted novelty 6.0

    LIMO achieves 63.3% on AIME24 and 95.6% on MATH500 via supervised fine-tuning on roughly 1% of the data used by prior models, supporting the claim that minimal strategic examples suffice when pre-training has already ...

  12. Search-o1: Agentic Search-Enhanced Large Reasoning Models

    cs.AI 2025-01 unverdicted novelty 6.0

    Search-o1 integrates agentic retrieval-augmented generation and a Reason-in-Documents module into large reasoning models to dynamically supply missing knowledge and improve performance on complex science, math, coding...

  13. HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

    cs.CL 2024-12 unverdicted novelty 6.0

    HuatuoGPT-o1 achieves superior medical complex reasoning by using a verifier to curate reasoning trajectories for fine-tuning and then applying RL with verifier-based rewards.

  14. Cognitive Duality for Adaptive Web Agents

    cs.AI 2025-08 unverdicted novelty 5.0

    A web agent called CogniWeb reportedly reaches 43.96% success on WebArena with 75% fewer tokens by switching between fast and slow reasoning, but the supplied body is a different paper and the claims could not be checked.

  15. Thought Graph Traversal for Test-time Scaling in Chest X-ray VLLMs

    cs.CV 2025-06 unverdicted novelty 5.0

    A new prompting framework called Thought Graph Traversal combined with reasoning budget forcing improves test-time performance of frozen chest X-ray VLLMs on report generation benchmarks.

  16. A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models

    cs.CL 2025-09 conditional novelty 4.0

    A structured literature survey concluding that reasoning capabilities do not automatically make LLMs more trustworthy and can introduce new vulnerabilities in safety, robustness, and privacy.

  17. From System 1 to System 2: A Survey of Reasoning Large Language Models

    cs.AI 2025-02 accept novelty 3.0

    The survey organizes the shift of LLMs toward deliberate System 2 reasoning, covering model construction techniques, performance on math and coding benchmarks, and future research directions.

  18. Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

    cs.AI 2025-01 unverdicted novelty 3.0

    The paper surveys reinforced reasoning techniques for LLMs, covering automated data construction, learning-to-reason methods, and test-time scaling as steps toward Large Reasoning Models.

  19. The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

    cs.AI 2026-06 unverdicted novelty 2.0

    A comprehensive reference book organizing existing techniques for agentic AI systems across LLM substrate, reasoning, agent design patterns, inter-agent coordination, and production deployment.

  20. The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

    cs.AI 2026-06 unverdicted novelty 1.0

    A survey-style reference book mapping the full agentic-AI stack from transformer internals to production deployment, with no new research result.