Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning

2026-07-24

AI Summary

This work addresses the limitation of existing large language model post-training methods in modeling self-evolutionary meta-skills—such as introspection driven by environmental feedback—hindering autonomous, continual improvement in open-ended scenarios. The authors propose a novel paradigm that integrates synthetic evolutionary trajectory data, verifiable reward-based reinforcement learning grounded in code execution feedback, and inference-time evolutionary search. For the first time, test case execution outcomes are leveraged as supervision signals to systematically train models for self-reflection and cross-domain generalization without explicit annotations. The approach achieves substantial performance gains across seven programming benchmarks, improving absolute accuracy by 10.01% on in-distribution tasks and by 24.12% on out-of-distribution tasks, with a remarkable 46.9% relative improvement on out-of-domain open-ended algorithmic optimization problems.

Abstract

Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains. We hypothesize that the success of such evolution frameworks hinges on meta-skills, such as self-reflection with environment feedback, that enable effective multi-round refinement, yet are largely neglected by traditional post-training. To bridge this gap, we present MetaEvolve, a framework designed to develop these meta-skills via a data synthesis pipeline, evolution-aware reinforcement learning (RL), and inference-time evolutionary search. Concretely, we ground MetaEvolve in coding, where program execution provides natural, continuous reward signals beyond binary correctness. Building on these signals, we synthesize evolution trajectories as training data, each containing a current program, its fitness score (combining correctness and efficiency), and a history of prior attempts, and train the model via RL with verifiable rewards derived from test case execution. By training on large-scale code data, we aim to inspire generalizable domain-agnostic meta-skills that can transfer broadly to open-ended problems where such rich training signals are scarce. Across seven coding benchmarks, MetaEvolve outperforms the strongest baseline by 10.01% absolute on in-distribution tasks and 24.12% on out-of-distribution tasks. On open-ended algorithm optimization problems entirely outside the training domain, it further achieves a 46.9% relative improvement. These results demonstrate that explicitly cultivating self-evolution meta-skills offers a principled path toward more capable and autonomously self-evolving AI.

Problem

• self-evolution
• meta-skills
• reinforcement learning
• test-time scaling
• environment feedback

Innovation

• self-evolution
• meta-skills
• reinforcement learning
• evolutionary search
• code execution feedback