ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning
📰 ArXiv cs.AI
Learn to optimize Large Reasoning Models using ThoughtFold, which reduces over-thinking by folding reasoning chains via introspective preference learning
Action Steps
- Apply ThoughtFold to fold reasoning chains in Large Reasoning Models
- Use Reinforcement Learning with Verifiable Rewards to train models on Chain-of-Thoughts
- Evaluate the performance of ThoughtFold in reducing over-thinking issues
- Configure the introspective preference learning module to optimize model performance
- Test the optimized model on various tasks to measure its efficiency
Who Needs to Know This
Researchers and engineers working on Large Reasoning Models can benefit from this approach to improve model efficiency and reduce over-thinking issues. This can be applied in teams focused on AI model development and optimization.
Key Insight
💡 ThoughtFold reduces over-thinking in Large Reasoning Models by folding reasoning chains, improving model efficiency and performance
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🤖 Introducing ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning to optimize Large Reasoning Models #AI #LLMs
Key Takeaways
Learn to optimize Large Reasoning Models using ThoughtFold, which reduces over-thinking by folding reasoning chains via introspective preference learning
Full Article
Title: ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning
Abstract:
arXiv:2606.03503v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have achieved remarkable progress thanks to Reinforcement Learning with Verifiable Rewards (RLVR) on Chain-of-Thoughts (CoTs). However, since long CoTs naturally contain trial and errors and mainstream RLVR approaches choose outcome-correct CoT trajectories for memorization, the redundant explorations in long CoTs are inevitably reinforced, which results in the over-thinking issues of LRMs. Previous attempts to resolve
Abstract:
arXiv:2606.03503v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have achieved remarkable progress thanks to Reinforcement Learning with Verifiable Rewards (RLVR) on Chain-of-Thoughts (CoTs). However, since long CoTs naturally contain trial and errors and mainstream RLVR approaches choose outcome-correct CoT trajectories for memorization, the redundant explorations in long CoTs are inevitably reinforced, which results in the over-thinking issues of LRMs. Previous attempts to resolve
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