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Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models
🤖AI Summary
Researchers discovered that large reasoning models (LRMs) suffer from inconsistent answers due to competing mechanisms between Chain-of-Thought reasoning and memory retrieval. They developed FARL, a new fine-tuning framework that suppresses retrieval shortcuts to promote genuine reasoning capabilities in AI models.
Key Takeaways
- →Large reasoning models often generate final answers that contradict their own reasoning processes.
- →Two competing mechanisms operate simultaneously: Chain-of-Thought reasoning and memory retrieval from training data.
- →Models can exploit retrieval mechanisms as shortcuts, undermining the development of genuine reasoning abilities.
- →The relative dominance of these mechanisms varies by problem domain, model scale, and fine-tuning approach.
- →FARL framework integrates memory unlearning with reinforcement learning to enhance reasoning-dominant behavior.
#large-language-models#reasoning#chain-of-thought#ai-training#machine-learning#fine-tuning#reinforcement-learning#arxiv#research
Read Original →via arXiv – CS AI
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