3 papers
cs.LG2026
Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards
Yuante Li, Junwei Quan, Kai Qiu +2
Reinforcement learning with verifiable rewards (RLVR) relies on costly model-generated responses, motivating larger rollout batches and square-root learning-rate scaling. Reported…
cs.AI2026
Are We Measuring Scientific Intelligence? Rethinking the Evaluation of AI Scientists
Kate Zhang, Yuante Li
AI agents can now carry out data-driven scientific analyses end to end, and benchmarks assess them by giving an agent a question and a dataset and scoring its final answer against…
cs.AI2026
Diverse Evidence, Better Forecasts: Multi-Agent Deliberation Under Information Asymmetry
Yuante Li, Yicheng Tao, Kate Zhang +3
Multi-agent systems are increasingly used for forecasting future events, as deliberation among multiple LLMs is believed to improve reasoning and calibration. Yet existing approach…