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.
Cite
@article{arxiv.2603.23951,
title = {From AI Assistant to AI Scientist: Autonomous Discovery of LLM-RL Algorithms with LLM Agents},
author = {Sirui Xia and Yikai Zhang and Aili Chen and Siye Wu and Siyu Yuan and Yanghua Xiao},
journal= {arXiv preprint arXiv:2603.23951},
year = {2026}
}