A naukri.com initiative
Arxiv
2d
105
Image Credit: Arxiv
RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling
- Rule-based reasoning is a fundamental problem, but variations in rule formats and complexity in real-world applications are challenging.
- Large reasoning models enhanced by reinforcement learning have shown remarkable capabilities.
- The effectiveness of small reasoning models in learning rule-based reasoning with generalization across tasks and domains remains an open question.
- A method called RuleReasoner is introduced to conduct rule-based reasoning with a wide range of tasks and domain-aware dynamic sampling.
- RuleReasoner resamples training batches by updating sampling weights based on historical rewards to facilitate domain augmentation and flexible learning schedules.
- Empirical evaluations show that RuleReasoner outperforms leading large reasoning models on in-distribution and out-of-distribution benchmarks.
- RuleReasoner achieves a significant performance improvement over existing methods on both in-distribution and out-of-distribution tasks.
- The approach also demonstrates higher computational efficiency compared to previous dynamic sampling methods for reinforcement learning.
Read Full Article
6 Likes
For uninterrupted reading, download the app