Zeming Liu

LLM agents can get 18% better at tasks by co-evolving their skills and tools, instead of learning them separately.

Forget monolithic policies – splitting your LLM's RL policy into accuracy-focused and exploration-driven modes unlocks better performance and diversity.

Open-source 7B LLMs can now rival GPT-4o performance on validation tasks, thanks to a novel reinforcement learning approach that leverages calibrated self-evaluation as a dense reward signal.