RaDeR: Reasoning-aware Dense Retrieval Models
This work addresses the challenge of improving retrieval for reasoning tasks in AI, offering a more data-efficient approach that could enhance retrieval-augmented language models.
The paper tackles the problem of dense retrieval for reasoning-intensive tasks by proposing RaDeR, a reasoning-aware dense retrieval model trained with LLM-derived data, which outperforms strong baselines on mathematical reasoning benchmarks and achieves comparable performance using only 2.5% of the training data of a concurrent method.
We propose RaDeR, a set of reasoning-based dense retrieval models trained with data derived from mathematical problem solving using large language models (LLMs). Our method leverages retrieval-augmented reasoning trajectories of an LLM and self-reflective relevance evaluation, enabling the creation of both diverse and hard-negative samples for reasoning-intensive relevance. RaDeR retrievers, trained for mathematical reasoning, effectively generalize to diverse reasoning tasks in the BRIGHT and RAR-b benchmarks, consistently outperforming strong baselines in overall performance. Notably, RaDeR achieves significantly higher performance than baselines on the Math and Coding splits. In addition, RaDeR presents the first dense retriever that outperforms BM25 when queries are Chain-of-Thought reasoning steps, underscoring the critical role of reasoning-based retrieval to augment reasoning language models. Furthermore, RaDeR achieves comparable or superior performance while using only 2.5% of the training data used by the concurrent work REASONIR, highlighting the quality of our synthesized training data.