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From AI Assistant to AI Scientist: Autonomous Discovery of LLM-RL Algorithms with LLM Agents

arXiv:2603.23951v1 Announce Type: new Abstract: Discovering improved policy optimization algorithms for language models remains a costly manual process requiring repeated mechanism-level modification and validation. Unlike simple combinatorial code search, this problem requires searching over algorithmic mechanisms tightly coupled with training dynamics while reusing empirical evidence across iterations. We propose POISE, a closed-loop framework for automated discovery of policy optimization algorithms for language models. POISE maintains a structured, genealogically linked archive linking proposals, executable implementations, standardized evaluations, and natural-language reflections to support evidence-driven iteration. In mathematical reasoning experiments starting from GRPO, POISE evaluates 64 candidate algorithms and discovers improved mechanisms, including analytic-variance scaling and validity masking. The best variant improves weighted Overall from 47.8 to 52.5 (+4.6) and inc

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Sirui Xia, Yikai Zhang, Aili Chen, Siye Wu, Siyu Yuan, Yanghua Xiao · · 1 min read · 5 views
#cs.CL

arXiv:2603.23951v1 Announce Type: new Abstract: Discovering improved policy optimization algorithms for language models remains a costly manual process requiring repeated mechanism-level modification and validation. Unlike simple combinatorial code search, this problem requires searching over algorithmic mechanisms tightly coupled with training dynamics while reusing empirical evidence across iterations. We propose POISE, a closed-loop framework for automated discovery of policy optimization algorithms for language models. POISE maintains a structured, genealogically linked archive linking proposals, executable implementations, standardized evaluations, and natural-language reflections to support evidence-driven iteration. In mathematical reasoning experiments starting from GRPO, POISE evaluates 64 candidate algorithms and discovers improved mechanisms, including analytic-variance scaling and validity masking. The best variant improves weighted Overall from 47.8 to 52.5 (+4.6) and increases AIME25 pass@32 from 26.7% to 43.3%, demonstrating the feasibility of automated policy optimization discovery while supporting interpretable design principles.

Executive Summary

The article proposes POISE, a closed-loop framework for automated discovery of policy optimization algorithms for language models. POISE maintains a structured archive linking proposals, implementations, evaluations, and reflections to support evidence-driven iteration. In experiments, POISE evaluates 64 candidate algorithms and discovers improved mechanisms, including analytic-variance scaling and validity masking, demonstrating the feasibility of automated policy optimization discovery. The framework's ability to reuse empirical evidence across iterations and support interpretable design principles makes it a significant contribution to the field. However, the article lacks a thorough discussion of the potential biases and limitations of the framework, particularly in terms of its reliance on pre-existing algorithms and the potential for over-reliance on standardized evaluations.

Key Points

  • ▸ POISE is a closed-loop framework for automated discovery of policy optimization algorithms for language models.
  • ▸ The framework maintains a structured archive linking proposals, implementations, evaluations, and reflections.
  • ▸ POISE evaluates 64 candidate algorithms and discovers improved mechanisms, including analytic-variance scaling and validity masking.

Merits

Strength in Evidence-Driven Iteration

POISE's ability to reuse empirical evidence across iterations supports interpretable design principles and enhances the discovery of improved mechanisms.

Improved Algorithmic Mechanisms

POISE discovers improved mechanisms, including analytic-variance scaling and validity masking, which demonstrate the feasibility of automated policy optimization discovery.

Demerits

Potential Biases and Limitations

The framework's reliance on pre-existing algorithms and standardized evaluations may introduce biases and limitations, which are not thoroughly discussed in the article.

Expert Commentary

The article's contribution to the field of natural language processing is significant, as it proposes a novel framework for automated discovery of policy optimization algorithms. However, the article's reliance on pre-existing algorithms and standardized evaluations may introduce biases and limitations that are not thoroughly discussed. Furthermore, the article could benefit from a more comprehensive discussion of the potential implications of POISE's adoption, particularly in terms of its potential impact on industries such as customer service and content moderation.

Recommendations

  • ✓ Future research should focus on addressing the potential biases and limitations of POISE, particularly in terms of its reliance on pre-existing algorithms and standardized evaluations.
  • ✓ The development of POISE should be accompanied by a thorough discussion of its potential implications, including its potential impact on industries such as customer service and content moderation.

Sources

Original: arXiv - cs.CL

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