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Autonomous ML engineering agents can achieve frontier-model performance by learning from long experimental trajectories using outcome-guided supervision and windowed reinforcement learning.
Achieving trillion-parameter performance with just 35 billion parameters by scaling agent horizons reveals a new frontier in model efficiency.
Forget RLHF, self-play, or chain-of-thought: geometric consensus with sparse supervision unlocks scientific reasoning in LLMs.