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AI Research
arXiv CS.AI·Jun 10·5 min read
Reasoning or Memorization? Direction-Aware Diversity Exploration in LLM Reinforcement Learning
TL;DR
Existing reinforcement learning methods for large language models (LLMs) often fail to differentiate between genuine reasoning and memorization during exploration. To address this, a new framework called Direction-Aware Reinforcement Learning (DiRL) was developed, which focuses exploration on reasoning-aligned trajectories.
✦ Why It Matters
Engineers can implement DiRL to improve reasoning in LLMs, enhancing their performance on complex tasks.
Key Takeaways
- Adopt DiRL to improve reasoning in LLMs.
- Utilize direction-weighted gradients for effective updates.
- Focus on shaping rewards to discourage memorization.
- Test DiRL on various reasoning benchmarks for validation.
How It Works
DiRL operates by extracting a direction from the model's internal representations that indicates whether the exploration is aligned with reasoning or memorization. It constructs direction-weighted gradient features that inform how the model updates its policy, ensuring that exploration favors reasoning processes.
By shaping the reward structure, DiRL amplifies exploration that leads to genuine reasoning improvements while suppressing variations that stem from memorized patterns.