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cs.AI2026
Function-Level Execution Feedback for Code Preference Optimization
Idris Nechnech, Sehwan Kim, Jimin Seo +4
Process supervision has improved mathematical reasoning, where intermediate steps are naturally expressed as chains of thought. In code generation, however, process supervision rem…
stat.ML2026
Deciding When to Switch: E-Processes for Adaptive Minimax Training for Generative Adversarial Nets
Hyunjoo Kim, Sicheng Wu, Agastya Venkatraman +2
Modern data science increasingly gives rise to hypothesis-testing problems that are not naturally formulated in terms of parameters within prespecified statistical models. One impo…