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cs.LG2026
Let the Batch Decide, On- or Off-Policy: Effective Sample Size for RL Post-Training
Rasool Fakoor, Murdock Aubry, Nicholas Stranges +1
Reinforcement learning (RL) is structurally harder than supervised learning because the policy changes the data distribution it learns from. The resulting fragility is especially v…
cs.LG2026
Understanding the Challenges in Iterative Generative Optimization with LLMs
Allen Nie, Xavier Daull, Zhiyi Kuang +10
Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach…