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LLM agents trained without any programmatic verifiers can actually outperform models trained on ground-truth reward signals when trajectory-level rubric judgments are dynamically decomposed into step-level advantages.
Forget training on narrow GitHub issues – Hybrid-Gym unlocks surprisingly broad coding skills by teaching agents to explore codebases and design architectures in synthetic environments.
