Track the frontier, from condensed matter to cosmology.
Search papers, labs, and topics across Lattice.
Affiliation:
3
0
6
Language agents can achieve superior sample efficiency and performance by jointly learning to act and forecast multi-step action sequences within a single policy backbone.
A learned "Token-to-Go" progress meter enables multi-turn RL agents to effectively learn from sparse rewards, boosting performance by up to 7.2 points without increasing training costs.
LLM agents can improve their decision-making by learning from their own diagnosed failures, with first-proposal corrections boosting success rates by over 30 percentage points.