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cs.LG2026
Spend Your Rollouts Where It Counts: Rollout Allocation for Group-Based RL Post-Training
Woojeong Kim, Ziyi Yang, Jing Nathan Yan +1
Reinforcement learning (RL) is the dominant paradigm for post-training large language models. However, in the online, on-policy setting, rollout generation dominates the computatio…
cs.LG2026
The Efficiency Gap in Byte Modeling
Celine Lee, Jing Nathan Yan, Chen Liang +9
Modern language models have historically relied on two dominant design choices: subword tokenization and autoregressive (AR) ordering. These design decisions bake in priors that di…