GASP: Guided Asymmetric Self-Play For Coding LLMs
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 of the student's learnability. Although these methods promise open-ended data generation bootstrapped from no human data, they suffer from one major problem: not all problems that are hard to solve are interesting or informative to improve the overall capabilities of the model. Current asymmetric self-play methods are goal-agnostic with no real grounding. We propose Guided Asymmetric Self-Play (GASP), where grounding is provided by real-data goalpost questions that are identified to pose a hard exploration chal...
| Authors | Swadesh Jana, Cansu Sancaktar, Tomáš Daniš et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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