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This work takes an RSI-inspired approach at the harness layer, scaling auto-research loops across increasingly numerous and diverse environments for harness rollouts, and yields reusable improvements that transfer beyond their development setting.
Retrieval strategies validated on one model can lead to significant performance gains in another, but only after rigorous experimental validation—VERDI makes this possible.
Self-improving agents can evolve autonomously with minimal human input, reshaping our approach to AI adaptability and deployment.
Smaller LLMs can learn to predict when they'll fail, paving the way for efficient "ask for help" systems that rival the performance of much larger models.
Forget agents and world models – the future of computing could be learned directly from I/O traces, turning the model itself into the computer.
Scale up offline policy training for diffusion LLMs without breaking the bank: dTRPO slashes trajectory computation costs while boosting performance up to 9.6% on STEM tasks.
