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cs.LG2025
Entropy After </Think> for reasoning model early exiting
Xi Wang, James McInerney, Lequn Wang +1
Reasoning LLMs show improved performance with longer chains of thought. However, recent work has highlighted their tendency to overthink, continuing to revise answers even after re…
cs.LG2025
Optimization of Epsilon-Greedy Exploration
Ethan Che, Hakan Ceylan, James McInerney +1
Modern recommendation systems rely on exploration to learn user preferences for new items, typically implementing uniform exploration policies (e.g., epsilon-greedy) due to their s…
cs.LG2024
Switching the Loss Reduces the Cost in Batch (Offline) Reinforcement Learning
Alex Ayoub, Kaiwen Wang, Vincent Liu +5
We propose training fitted Q-iteration with log-loss (FQI-log) for batch reinforcement learning (RL). We show that the number of samples needed to learn a near-optimal policy with…