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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.
Forget scaling laws: this work shows you can get SOTA reasoning from sub-billion parameter models with *less* data, if you're smart about curation and resampling.
