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AdaptR1 framework cuts LLM reasoning costs with RL

By PulseAugur Editorial · [2 sources] ·

Researchers have developed AdaptR1, a novel framework that uses reinforcement learning to optimize reasoning in large language models for multi-hop question answering. This approach dynamically allocates reasoning budgets at each step, unlike prior methods that make a single query-level decision. AdaptR1 significantly reduces the number of "think tokens" generated, leading to lower inference costs while maintaining or improving performance on tasks like HotpotQA. AI

IMPACT Reduces inference costs for complex LLM reasoning tasks by optimizing token usage.

RANK_REASON The cluster contains an academic paper detailing a new research framework for LLMs.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Yuxin Wang, Jiahao Lu, Qifeng Wu, Shicheng Fang, Chuanyuan Tan, Yining Zheng, Xuanjing Huang, Xipeng Qiu ·

    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…

  2. arXiv cs.CL TIER_1 English(EN) · Xipeng Qiu ·

    AdaptR1: Reinforcement Learning Based Adaptive Interleaved Thinking in Multi-hop Question Answering

    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 a…

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