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AI Research
arXiv CS.AI·Jun 9·5 min read
INFUSER: Influence-Guided Self-Evolution Improves Reasoning
TL;DR
Existing self-evolution methods for language models often rely on curated data or ineffective unsupervised rewards. INFUSER is introduced as an iterative co-training framework where a Generator creates questions and answers, enhancing the model's reasoning capabilities.
✦ Why It Matters
Engineers can leverage INFUSER to enhance language model reasoning with minimal data requirements.
Key Takeaways
- Explore the INFUSER framework for improved reasoning in language models.
- Utilize the dual-role Generator and Solver approach for adaptive learning.
- Implement DuGRPO for effective training of question-generating models.
- Test your models against Olympiad and SuperGPQA benchmarks for performance evaluation.
How It Works
INFUSER operates through a co-training mechanism where the Generator drafts questions and answers from a document pool, while the Solver learns from these drafts. The Generator is guided by an influence score that assesses the potential of each question to improve the Solver's performance, ensuring that the training focuses on questions that are not just difficult but also beneficial for learning.