AdaptR1: Reinforcement Learning Based Adaptive Interleaved Thinking in Multi-hop Question Answering
arXiv:2605.31062v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable performance in complex reasoning tasks through Chain-of-Thought (CoT) prompting. However, this approach often leads to ``over-thinking,'' where models generate unnecessarily long reasoning traces for simple queries and incur avoidable inference cost. While recent work has explored adaptive reasoning, existing methods typically make a single query-level decision about whether to reason. This overlooks the dynamic nature of multi-step tasks, where the need for explicit reasoning varies across in
The continuous drive to optimize LLM performance and cost efficiency is leading to more sophisticated reasoning strategies, especially as models are integrated into complex agentic workflows.
This research addresses a key limitation of current LLM reasoning by reducing 'over-thinking,' which can significantly lower inference costs and improve the practical deployability of AI systems for multi-step tasks.
LLMs can now adaptively decide when and how much to reason during multi-hop tasks, moving beyond static, query-level decisions to more dynamic and efficient reasoning across an entire workflow.
- · LLM developers
- · Cloud providers (reduced compute costs for LLMs)
- · AI agent developers
- · Businesses using LLMs for complex workflows
Adaptive reasoning will make LLMs more cost-effective and faster for complex, multi-step problem-solving.
Improved efficiency could accelerate the development and deployment of sophisticated AI agents across various industries.
The reduced computational overhead may lower the barrier to entry for developing and maintaining advanced AI applications, potentially increasing market competition and innovation.
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Read at arXiv cs.CL