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cs.LG2026
IsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL
Zhoujun Cheng, Yutao Xie, Yuxiao Qu +12
While scaling laws guide compute allocation for LLM pre-training, analogous prescriptions for reinforcement learning (RL) post-training of large language models (LLMs) remain poorl…
cs.LG2026
Improving and Accelerating Offline RL in Large Discrete Action Spaces with Structured Policy Initialization
Matthew Landers, Taylor W. Killian, Thomas Hartvigsen +1
Reinforcement learning in discrete combinatorial action spaces requires searching over exponentially many joint actions to simultaneously select multiple sub-actions that form cohe…