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EvoTrainer introduces an autonomous training framework that co-evolves LLM policies and training harnesses through empirical feedback, outperforming human-engineered RL baselines on mathematical reasoning, code generation, and long-horizon software engineering tasks.
A methodology for autonomously training transformer language models on a single consumer GPU, structured in six stages with verification gates and AGENTS.md specs for orchestration frameworks like OpenClaw.