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cs.LG2026
A Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula
Cansu Sancaktar, David Zhang, Gabriel Synnaeve +1
Reinforcement learning (RL) has emerged as a powerful paradigm for improving large language models beyond supervised fine-tuning, yet sustaining performance gains at scale remains…
cs.LG2026
GASP: Guided Asymmetric Self-Play For Coding LLMs
Swadesh Jana, Cansu Sancaktar, Tomáš Daniš +3
Asymmetric self-play has emerged as a promising paradigm for post-training large language models, where a teacher continually generates questions for a student to solve at the edge…