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A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning

Mengqi Li, Lei Zhao, Anthony Man-Cho So, Ruoyu Sun, Xiao Li · Oct 21, 2025 · Citations: 0

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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training? We show that they can. We propose Self-evolving Post-Training (SePT), a simple post-training method that alternates between self-generation and training on self-generated responses. It repeatedly samples questions, uses the model itself to generate low-temperature responses, and then finetunes the model on the self-generated data. In this self-training loop, we use an online data refresh mechanism, where each new batch is generated by the most recently updated model. Across six math reasoning benchmarks, SePT improves a strong no-training baseline, defined as the untuned base model evaluated at its best swept decoding temperature, on several tested models. In some settings, SePT can even approach the performance of Reinforcement Learning with Verifiable Rewards (RLVR). Additional ablations demonstrate the importance of online data refresh and temperature decoupling. Overall, our results identify a practical regime in which reasoning can be improved using self-generated supervision alone. Our code is available at https://github.com/ElementQi/SePT.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
  • The abstract does not clearly describe the evaluation setup.
  • The abstract does not clearly name benchmarks or metrics.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 15%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training?"

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training?"

Quality Controls

missing

Not reported

No explicit QC controls found.

"Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training?"

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training?"

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training?"

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: Math, Coding

Evaluation Details

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training?

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training?
  • We propose Self-evolving Post-Training (SePT), a simple post-training method that alternates between self-generation and training on self-generated responses.
  • It repeatedly samples questions, uses the model itself to generate low-temperature responses, and then finetunes the model on the self-generated data.

Researcher Actions

  • Compare this paper against others mentioning MATH.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

MATH

Research Summary

Contribution Summary

  • We show that they can.
  • We propose Self-evolving Post-Training (SePT), a simple post-training method that alternates between self-generation and training on self-generated responses.
  • Across six math reasoning benchmarks, SePT improves a strong no-training baseline, defined as the untuned base model evaluated at its best swept decoding temperature, on several tested models.

Why It Matters For Eval

  • Across six math reasoning benchmarks, SePT improves a strong no-training baseline, defined as the untuned base model evaluated at its best swept decoding temperature, on several tested models.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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