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cs.LG2026
Verifier-Grounded Agentic RL for Provably Correct Code
George Ma, Yuhao Zhang, Bing He +5
Reinforcement learning for code usually rewards passing tests, which a model can do while remaining wrong on the inputs the tests do not cover. A program verifier instead decides w…
cs.LG2026
Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models
Guanghan Wang, Bing He, Zhan Shi +3
Diffusion language models (DLMs) have attracted wide attention for their fast, parallel decoding, but they are believed to be far less efficient to train than autoregressive langua…