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Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models

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arxiv 2506.06395 v3 pith:ZTCDUZCN submitted 2025-06-05 cs.CL cs.LG

Pengyi Li , Matvey Skripkin , Alexander Zubrey , Andrey Kuznetsov , Ivan Oseledets This is my paper
classification cs.CLcs.LG
keywords modelsrewardrlscconfidencelanguagelearningneedonly
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) excel at reasoning, yet post-training remains critical for aligning their behavior with task goals. Existing reinforcement learning (RL) methods often depend on costly human annotations or external reward models. We propose Reinforcement Learning via Self-Confidence (RLSC), which uses the model's own confidence as reward signals-eliminating the need for labels, preference models, or reward engineering. Applied to Qwen2.5-Math-7B with only 16 samples per question and 10 or 20 training steps, RLSC improves accuracy by +13.4% on AIME2024, +21.2% on MATH500, +21.7% on Minerva Math, +20.8% on Olympiadbench, and +9.7% on AMC23. RLSC provides a simple, scalable post-training method for inference models, requiring only a small number of samples and unlabelled supervision.

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Cited by 7 Pith papers

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

  1. Relationship-Centered Care: Relatedness and Responsible Design for Human Connections in Mental-Health Care

    cs.HC 2026-03 accept novelty 7.0 of 10

    Length-aware GFlowNet matching of α-power base distributions elicits either stronger LLM reasoning (α>1) or restored creativity (α<1) without external supervision, matching or beating RLIF and GRPO baselines.

  2. CPMobius: Iterative Coach-Player Reasoning for Data-Free Reinforcement Learning

    cs.CL 2026-02 conditional novelty 6.0 of 10

    CPMobius uses iterative coach-player reinforcement learning to improve mathematical reasoning in LLMs without external training data, yielding +4.9 average accuracy gains on Qwen2.5-Math-7B-Instruct.

  3. Breaking the Self-Confirming Loop: Diagnosing and Mitigating Systemic Reward Bias in Self-Rewarding RL

    cs.LG 2025-10 conditional novelty 6.0 of 10

    Self-rewarding RL can be stabilized by ensembling multiple policy models' majority-vote rewards, reaching within 3.6% of verifiable-reward RL on math benchmarks.

  4. Self-Evolving Vision-Language Models for Image Quality Assessment via Voting and Ranking

    cs.CV 2025-09 conditional novelty 6.0 of 10

    EvoQuality lets a VLM self-train for image quality assessment using majority-voted pairwise preferences as pseudo-labels, improving PLCC by about 32% and rivaling supervised models.

  5. Maximizing Prefix-Confidence at Test-Time Efficiently Improves Mathematical Reasoning

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Selecting the most confident 32-token prefix and completing only it gives better accuracy per compute than majority voting on five math reasoning datasets, using only the model's own confidence as a selector.

  6. No Free Lunch: Rethinking Internal Feedback for LLM Reasoning

    cs.LG 2025-06 conditional novelty 5.0 of 10

    Internal feedback rewards (entropy and self-certainty) improve base LLM math reasoning only in early training and degrade later, with little benefit for instruct models.

  7. Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle

    cs.CL 2025-09 conditional novelty 3.0 of 10

    A survey that maps reinforcement learning methods, datasets, benchmarks, and open-source tools across the full training lifecycle of large language models, focusing on verifiable-reward reasoning.

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