Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning

A practical approach to activate long chain-ofthoughts reasoning ability in large language models is to perform supervised fine-tuning on instruction datasets synthesized by strong large reasoning models, offering a cost-effective alternative to reinforcement learning.However, large-scale instruction sets incur significant training overhead, while effective strategies for automatic data selection still remain unexplored.We propose SELECT2REASON, a novel and efficient instruction-tuning data selection framework for long-CoT reasoning.From the perspective of emergence of rethinking behaviors like self-correction and backtracking, we investigate metrics that may determine the quality of long-CoT instructions.SELECT2REASON leverages a difficulty-aware reward model to estimate the learning value of questions and jointly incorporates a reasoning trace length-based heuristic through a weighted scheme for ranking to prioritize high-utility examples.Empirical results on OpenR1-Math-220k demonstrate that fine-tuning LLM on only 10% of the data selected by our method achieves performance competitive with or superior to full-data tuning and open-source baseline across nine competition-level mathematical benchmarks and four broader reasoning tasks.Further experiments highlight the scalability in varying data size, efficiency during inference, and adaptability to other instruction pools of SELECT2REASON with minimal cost.