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 this issue mainly give more advantage to shorter trajectories, yet their learning signals are still
The proliferation of advanced AI research necessitates more efficient and optimized large reasoning models to overcome current computational inefficiencies.
Improving the efficiency of large reasoning models can significantly reduce computational costs and accelerator demands for AI, impacting both training and inference.
This research introduces a novel approach to optimize AI reasoning chains, potentially leading to more efficient and less 'over-thinking' autonomous AI agents.
- · AI developers
- · Cloud providers
- · AI-powered SaaS companies
- · General AI research
- · Legacy AI models with inefficient reasoning
- · Companies reliant on brute-force computational scaling
More cost-effective and capable AI models due to optimized reasoning processes.
Accelerated development and deployment of complex AI agents and autonomous systems.
Enhanced accessibility and widespread adoption of sophisticated AI across various industries due to lower barriers to entry.
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Read at arXiv cs.AI