Search papers, labs, and topics across Lattice.
3
0
7
Coding agents can now evolve their own harnesses to outperform human-designed ones, thanks to a novel observability-driven approach.
Learned critics in RLHF can actually *increase* variance and hurt performance in sparse-reward settings, but a simple explained variance metric can tell you when to ditch the critic and get better results.
Multi-turn reinforcement learning gets a boost: weighting trajectories by semantic similarity dramatically improves baseline estimation and agent performance in long-document visual QA.
