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