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AI research agents automate the cycle of proposing, implementing, and evaluating experiments, opening a path toward recursive self-improvement. Yet their ability to propose experiments outpaces their capacity to execute them in real environments, making outcome prediction a key capability for sustained self-improvement...
Results provide evidence that access to a scalable, objective training signal can be a more important constraint than model scale alone, and can make otherwise ambiguous diagnostic reasoning tasks amenable to scalable reinforcement learning.
This is the first publicly reported autonomous post-training run at this scale, where prior public autonomous-ML-research demonstrations sit at GPT-2-class (~124M) budgets, and is treated as direct, auditable evidence that a scaled autonomous loop can produce discovery, not only optimization.
This paper proposes World Model RL (WMRL), which replaces environment execution with a world model to remove this bottleneck and accelerates training by 3-4x on various tasks at different agent scales, while exceeding the performance of standard RL baselines.
This formulation enables a systematic study of key self-improvement factors through the proposed Evo-Harness, and provides a principled understanding of how LLM agents can effectively learn on the fly.
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