Academic

Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards

arXiv:2603.09117v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from calibration degeneration, where models become excessively over-confident in incorrect answers. Previous studies devote to directly incorporating calibration objective into existing optimization target. However, our theoretical analysis demonstrates that there exists a fundamental gradient conflict between the optimization for maximizing policy accuracy and minimizing calibration error. Building on this insight, we propose DCPO, a simple yet effective framework that systematically decouples reasoning and calibration objectives. Extensive experiments demonstrate that our DCPO not only preserves accuracy on par with GRPO but also achieves the best calibration performance and substantially mitigates the over-confidence issue. Our study provides valuable insights and practical solution for more reliable

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Zhengzhao Ma, Xueru Wen, Boxi Cao, Yaojie Lu, Hongyu Lin, Jinglin Yang, Min He, Xianpei Han, Le Sun · · 1 min read · 10 views
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arXiv:2603.09117v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from calibration degeneration, where models become excessively over-confident in incorrect answers. Previous studies devote to directly incorporating calibration objective into existing optimization target. However, our theoretical analysis demonstrates that there exists a fundamental gradient conflict between the optimization for maximizing policy accuracy and minimizing calibration error. Building on this insight, we propose DCPO, a simple yet effective framework that systematically decouples reasoning and calibration objectives. Extensive experiments demonstrate that our DCPO not only preserves accuracy on par with GRPO but also achieves the best calibration performance and substantially mitigates the over-confidence issue. Our study provides valuable insights and practical solution for more reliable LLM deployment.

Executive Summary

This paper proposes a novel framework, DCPO, to address the issue of calibration degeneration in Reinforcement Learning from Verifiable Rewards (RLVR), a technique used to enhance large language model (LLM) reasoning. The authors identify a fundamental gradient conflict between optimization for policy accuracy and calibration error, and develop a decoupled framework to systematically address this issue. The approach preserves accuracy while achieving the best calibration performance and mitigating over-confidence. The study provides valuable insights and practical solutions for more reliable LLM deployment, addressing a critical issue in the field. The proposed framework has the potential to improve the reliability and trustworthiness of LLMs in real-world applications.

Key Points

  • ▸ DCPO decouples reasoning and calibration objectives in RLVR
  • ▸ The framework addresses the issue of calibration degeneration and over-confidence
  • ▸ Preserves accuracy while achieving the best calibration performance

Merits

Strength in Addressing Calibration Degeneration

The proposed framework effectively addresses the issue of calibration degeneration and over-confidence in RLVR, a critical problem in the field. The approach provides a systematic solution to this issue, which has significant implications for the reliability and trustworthiness of LLMs.

Preservation of Policy Accuracy

The DCPO framework preserves policy accuracy, ensuring that the proposed solution does not compromise the performance of the LLM in terms of reasoning ability.

Demerits

Limited Experimental Scope

The paper's experimental scope is limited to a specific set of tasks and models, which may not generalize to other domains or more complex tasks. Further experimentation and validation are required to confirm the effectiveness of the proposed framework across a broader range of scenarios.

Theoretical Complexity

The proposed framework introduces a new optimization objective, which may increase the computational complexity of the RLVR algorithm. Further research is needed to investigate the scalability and efficiency of the DCPO framework in practice.

Expert Commentary

The proposed DCPO framework is a significant contribution to the field of RLVR and AI research. By decoupling reasoning and calibration objectives, the framework provides a systematic solution to the issue of calibration degeneration and over-confidence in LLMs. The study's findings have far-reaching implications for the reliable deployment of AI systems and highlight the need for more transparent and trustworthy AI models. While the proposed framework has significant potential, further experimentation and validation are required to confirm its effectiveness across a broader range of scenarios.

Recommendations

  • ✓ Further experimentation and validation are required to confirm the effectiveness of the proposed framework across a broader range of scenarios.
  • ✓ The study's findings have significant implications for policy and regulatory frameworks governing the development and deployment of AI systems, highlighting the need for more stringent requirements for calibration and trustworthiness in AI models.

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