KnowRL: Boosting LLM Reasoning via Reinforcement Learning with Minimal-Sufficient Knowledge Guidance
The realm of artificial intelligence is rapidly evolving, with new frameworks and methodologies emerging to enhance the capabilities of large language models (LLMs). One such innovative approach is KnowRL, short for Knowledge-Guided Reinforcement Learning, which aims to improve reasoning in LLMs through an efficient reinforcement learning training framework.
Recently outlined in the preprint arXiv:2604.12627v1, KnowRL addresses critical challenges in reinforcement learning, particularly the issue of reward sparsity observed in complex reasoning tasks. Traditional reinforcement learning with large language models often struggles to yield effective training outcomes due to the infrequency of rewards in difficult problem-solving scenarios.
Abstract Overview
The research emphasizes the limitations of existing hint-based reinforcement learning methods, which attempt to alleviate reward sparsity by introducing partial solutions or abstract templates. However, these methods tend to scale guidance by simply adding more tokens. This can lead to several issues, including redundancy, inconsistency, and increased training overhead.
Key Innovations of KnowRL
KnowRL introduces a paradigm shift by treating the design of hints as a minimal-sufficient guidance problem. The framework operates on several key principles:
- Atomic Knowledge Points (KPs): KnowRL decomposes guidance into atomic knowledge points, which are the fundamental units of knowledge necessary for effective reasoning.
- Constrained Subset Search (CSS): This method is employed to construct compact and interaction-aware subsets of KPs for training, ensuring that the model learns from the most relevant information.
- Pruning Interaction Paradox: The framework identifies a paradox where the removal of a single KP may enhance performance, but the removal of multiple KPs can adversely affect it. KnowRL explicitly optimizes for robust subset curation under this interdependence.
Performance and Results
In testing, KnowRL was utilized to train KnowRL-Nemotron-1.5B, based on the OpenMath-Nemotron-1.5B model. The results across eight reasoning benchmarks demonstrate a significant improvement in performance at the 1.5B scale. Key findings include:
- KnowRL-Nemotron-1.5B achieved an average accuracy of 70.08% without the use of KP hints during inference, surpassing the previous Nemotron-1.5B by +9.63 points.
- With selected KPs, the performance further improved to 74.16%, establishing a new state-of-the-art in this domain.
Availability
The model, curated training data, and code are publicly accessible at https://github.com/Hasuer/KnowRL. Researchers and practitioners in the field of AI can leverage this resource to explore the capabilities of KnowRL and further enhance LLM reasoning.
Conclusion
KnowRL represents a significant advancement in the field of reinforcement learning for large language models. By addressing the challenges of reward sparsity and optimizing hint guidance, KnowRL has the potential to set new standards for reasoning capabilities in AI systems.