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GASP: Guided Asymmetric Self-Play For Coding LLMs

arXiv:2603.15957v1 Announce Type: new Abstract: 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 challenge to the model. During self-play, the teacher first generates an easier variant of a hard question, and then a harder variant of that easier question, with the goal of gradually closing the gap to the goalpost throughout training. Doing so, we improv

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Swadesh Jana, Cansu Sancaktar, Tom\'a\v{s} Dani\v{s}, Georg Martius, Antonio Orvieto, Pavel Kolev · · 1 min read · 8 views
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arXiv:2603.15957v1 Announce Type: new Abstract: 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 challenge to the model. During self-play, the teacher first generates an easier variant of a hard question, and then a harder variant of that easier question, with the goal of gradually closing the gap to the goalpost throughout training. Doing so, we improve pass@20 on LiveCodeBench (LCB) by 2.5% over unguided asymmetric self-play, and through the curriculum constructed by the teacher, we manage to solve hard goalpost questions that remain out of reach for all baselines.

Executive Summary

The article introduces Guided Asymmetric Self-Play (GASP), a novel approach to post-training large language models. Building upon existing asymmetric self-play methods, GASP incorporates real-data goalpost questions that challenge the model to explore and improve its capabilities. By gradually increasing the difficulty level through a teacher-led curriculum, GASP demonstrates improved performance on the LiveCodeBench (LCB) and solves hard goalpost questions that previous methods failed to address. While GASP shows promising results, its potential applications and limitations warrant further investigation. This commentary will delve into the merits and demerits of GASP, its connections to related issues, and its practical and policy implications.

Key Points

  • ▸ GASP is a guided approach to asymmetric self-play that incorporates real-data goalpost questions.
  • ▸ The teacher-led curriculum gradually increases the difficulty level, pushing the model to explore and improve.
  • ▸ GASP demonstrates improved performance on LiveCodeBench (LCB) and solves hard goalpost questions that previous methods failed to address.

Merits

Improved Performance

GASP achieves a 2.5% improvement in pass@20 on LiveCodeBench (LCB) compared to unguided asymmetric self-play, demonstrating its effectiveness in improving model performance.

Goal-Oriented Approach

GASP's use of real-data goalpost questions provides a goal-oriented approach, ensuring that the model is challenged to explore and improve its capabilities in a meaningful way.

Curriculum-Based Learning

The teacher-led curriculum in GASP allows for gradual increases in difficulty, enabling the model to learn and improve in a structured and efficient manner.

Demerits

Limited Generalizability

GASP's performance may be limited to specific domains or tasks, requiring further investigation to determine its generalizability to other areas.

Dependence on High-Quality Goalpost Questions

The success of GASP relies heavily on the quality and relevance of the goalpost questions, which may be challenging to obtain or maintain in certain contexts.

Expert Commentary

GASP's integration of real-data goalpost questions and teacher-led curriculum learning demonstrates a promising approach to improving large language model performance. While its merits are clear, further investigation is necessary to address its limitations and ensure its generalizability to various domains and tasks. The connections to transfer learning and curriculum learning approaches also warrant exploration, as they may provide valuable insights into the development of more effective learning strategies. Overall, GASP's potential applications and implications make it an exciting area of research, and its further development and refinement are likely to have significant impacts on the field of AI.

Recommendations

  • ✓ Further investigation into GASP's generalizability to various domains and tasks is necessary to determine its full potential and limitations.
  • ✓ The development of more effective and targeted approaches to obtaining high-quality goalpost questions is crucial to the success of GASP and similar methods.

Sources

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