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arxiv: 2412.09413 · v2 · pith:R5R6G44Qnew · submitted 2024-12-12 · 💻 cs.AI · cs.CL

Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems

Yingqian Min , Zhipeng Chen , Jinhao Jiang , Jie Chen , Jia Deng , Yiwen Hu , Yiru Tang , Jiapeng Wang
show 6 more authors
Xiaoxue Cheng Huatong Song Wayne Xin Zhao Zheng Liu Zhongyuan Wang Ji-Rong Wen
This is my paper

Pith reviewed 2026-05-18 00:31 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords slow-thinking reasoningreasoning reproductionself-improvementmultiple rolloutso1-like systemsAI reasoning benchmarks
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The pith

Reproducing o1-like slow-thinking reasoning works by first imitating long thought traces, then exploring hard problems with multiple rollouts, and iteratively refining the training set on its own outputs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to show that a three-step public recipe can close much of the gap to closed industry reasoning models. First the base model is fine-tuned on distilled long-form reasoning traces so it learns to spend extra steps thinking. It is then prompted to generate many solutions to difficult questions, keeping only the correct ones as new training data. Finally the model retrains on this growing set and repeats the cycle. A sympathetic reader cares because the method is fully described and does not rely on secret data or proprietary training runs, so others can test and extend it.

Core claim

By first fine-tuning on distilled long-form thought data to invoke a slow-thinking mode, then generating multiple rollouts on challenging problems to harvest high-quality correct trajectories, and finally using those trajectories to iteratively refine the training dataset, the resulting STILL-2 model reaches competitive accuracy on three hard reasoning benchmarks.

What carries the argument

The STILL-2 framework that sequences imitation of long thought traces, exploration via multiple rollouts, and self-improvement through iterative dataset refinement.

If this is right

  • The fine-tuned model learns to produce extended internal reasoning before giving a final answer.
  • Multiple rollouts on the same hard question yield an increasing fraction of correct solution paths.
  • Each self-improvement round raises performance on the chosen benchmarks.
  • The final system matches the accuracy of undisclosed industry reasoning models on the tested tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the self-refinement loop stays stable, the method could be applied to new domains with only a small seed of high-quality traces.
  • The approach suggests that closed models may rely on similar internal search-and-filter steps that are now reproducible from public data.
  • Monitoring error accumulation across iterations would be a natural next measurement to decide how many rounds are safe.

Load-bearing premise

That generating many rollouts on hard problems will keep producing more correct trajectories and that retraining on the model's own outputs will steadily raise quality without accumulating mistakes.

What would settle it

Run the full imitation-explore-refine loop for three cycles and measure whether accuracy on the hardest benchmark either plateaus below or falls behind the reported industry baselines.

read the original abstract

Recently, slow-thinking reasoning systems, such as o1, have demonstrated remarkable capabilities in solving complex reasoning tasks. These systems typically engage in an extended thinking process before responding to a query, allowing them to generate more thorough, accurate, and well-reasoned solutions. These systems are primarily developed and maintained by industry, with their core techniques not publicly disclosed. In response, an increasing number of studies from the research community aim to explore the technical foundations underlying these powerful reasoning systems. Building on these prior efforts, this paper presents a reproduction report on implementing o1-like reasoning systems. We introduce an ``imitate, explore, and self-improve'' framework, denoted as \textbf{STILL-2}, as our primary technical approach to train the reasoning model. In the initial phase, we use distilled long-form thought data to fine-tune the reasoning model, enabling it to invoke a slow-thinking mode. The model is then encouraged to explore challenging problems by generating multiple rollouts, which can result in increasingly more high-quality trajectories that lead to correct answers. Furthermore, the model undergoes self-improvement by iteratively refining its training dataset. To verify the effectiveness of this approach, we conduct extensive experiments on three challenging benchmarks. The experimental results demonstrate that our approach achieves competitive performance compared to industry-level reasoning systems on these benchmarks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents STILL-2, an 'imitate, explore, and self-improve' framework for reproducing o1-like slow-thinking reasoning systems. It begins with fine-tuning a base model on distilled long-form thought data to induce slow-thinking behavior, proceeds to an exploration phase that generates multiple rollouts on challenging problems to surface additional high-quality correct trajectories, and concludes with iterative self-improvement that refines the training dataset using the model's own outputs. The central claim is that this pipeline yields competitive performance on three challenging benchmarks relative to industry-level reasoning systems.

Significance. If the reported performance gains are robustly supported by detailed, reproducible experiments with proper controls, the work would be significant as one of the first public, end-to-end reproductions of extended chain-of-thought reasoning systems. It offers a concrete, open recipe that combines imitation learning with self-generated trajectories, which could lower barriers for community research on scaling reasoning capabilities beyond standard supervised fine-tuning.

major comments (2)
  1. [§4 (Experiments)] §4 (Experiments) and associated tables: the assertion of 'competitive performance' is not accompanied by concrete accuracy numbers, baseline comparisons (e.g., against standard CoT or prior open reproductions), error bars, or details on data sources and exclusion rules, leaving the headline claim without visible empirical grounding.
  2. [Self-improve stage (§3.3)] Self-improve stage description (around §3.3): the iterative refinement of the training dataset assumes that filtering or self-labeling multiple rollouts on hard problems will reliably increase the proportion of correct trajectories without compounding errors, yet no external verifier, held-out accuracy check, or success-rate bound on the rollout filter is provided; this assumption is load-bearing for attributing gains to STILL-2 rather than to the initial imitation phase.
minor comments (1)
  1. [Abstract and §3] The abstract and method sections use the term 'high-quality trajectories' without an explicit operational definition or filtering criterion, which could be clarified for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our reproduction report. We address each major comment below and outline the revisions we will make to improve the clarity and rigor of the experimental results and the self-improvement methodology.

read point-by-point responses

  1. Referee: §4 (Experiments) and associated tables: the assertion of 'competitive performance' is not accompanied by concrete accuracy numbers, baseline comparisons (e.g., against standard CoT or prior open reproductions), error bars, or details on data sources and exclusion rules, leaving the headline claim without visible empirical grounding.

    Authors: We appreciate this observation. The manuscript reports concrete accuracy numbers for STILL-2 on the three benchmarks in the experimental tables, along with comparisons to industry systems such as o1-preview. To further ground the claim, we will revise §4 to add explicit baseline results against standard Chain-of-Thought and prior open reproductions, include error bars from repeated runs where computationally feasible, and expand the description of data sources and exclusion rules. revision: yes

  2. Referee: Self-improve stage description (around §3.3): the iterative refinement of the training dataset assumes that filtering or self-labeling multiple rollouts on hard problems will reliably increase the proportion of correct trajectories without compounding errors, yet no external verifier, held-out accuracy check, or success-rate bound on the rollout filter is provided; this assumption is load-bearing for attributing gains to STILL-2 rather than to the initial imitation phase.

    Authors: The referee correctly notes that the self-improvement stage depends on the quality of filtered trajectories. In §3.3 we select rollouts that produce correct final answers using ground-truth verification on the training problems. We will revise the section to include a held-out accuracy analysis showing the increase in correct trajectories across iterations and report the empirical success rate of the rollout filter to quantify and bound potential error accumulation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical reproduction uses external benchmarks and standard self-training without definitional reduction

full rationale

The paper describes an imitate-explore-self-improve pipeline (STILL-2) that begins with fine-tuning on externally distilled long-form thought data, proceeds to multiple rollouts on challenging problems to collect trajectories, and iterates dataset refinement. Performance is measured on three external benchmarks and compared to industry systems. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text that would make any claimed result equivalent to its inputs by construction. The central claims rest on experimental outcomes rather than internal redefinitions or load-bearing self-references, rendering the derivation chain self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard machine-learning assumptions about the benefits of supervised fine-tuning on long-form data and the value of self-generated trajectories for iterative improvement; no new entities or ad-hoc parameters are introduced in the abstract.

axioms (2)
  • domain assumption Distilled long-form thought data from external sources provides a sufficient starting point for invoking slow-thinking behavior in the base model.
    Invoked in the initial imitation phase described in the abstract.
  • domain assumption Multiple rollouts on challenging problems will yield progressively higher-quality correct trajectories suitable for further training.
    Central to the explore phase and self-improvement loop.

pith-pipeline@v0.9.0 · 5817 in / 1271 out tokens · 40496 ms · 2026-05-18T00:31:17.215529+00:00 · methodology

discussion (0)

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