arXiv:2603.17310v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary computational cost. While existing reinforcement learning approaches address this by optimizing final response length, they neglect the quality of intermediate reasoning steps, leaving models vulnerable to reward hacking. We argue that verbosity is not merely a length problem, but a symptom of poor intermediate reasoning quality. To investigate this, we conduct an empirical study tracking the per
The proliferation of complex LLM applications necessitates more efficient and reliable reasoning processes to manage computational costs and improve output quality.
Improving the efficiency of LLM reasoning directly impacts the cost and scalability of AI systems, making advanced AI applications more viable for broader deployment.
This research shifts the focus from merely optimizing final response length to enhancing the quality of intermediate reasoning steps, leading to more robust and less 'reward-hacked' AI.
- · AI developers
- · Cloud providers
- · Enterprises deploying LLMs
- · Researchers in AI efficiency
- · Inefficient LLM architectures
- · Companies with high compute costs
More efficient LLMs will reduce operational costs for AI-powered services.
The cost savings could enable wider adoption of sophisticated AI reasoning in new applications and industries.
Increased accessibility to advanced reasoning might accelerate the development of more autonomous and capable AI agents.
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Read at arXiv cs.CL