Advancing LLM Reasoning Generalists with Preference Trees
Lifan Yuan Ganqu Cui Hanbin Wang Ning Ding Xingyao Wang Jia Deng Boji Shan Huimin Chen Ruobing Xie Yankai Lin Zhenghao Liu Bowen Zhou Hao Peng Zhiyuan Liu Maosong Sun
We introduce Eurus, a suite of large language models (LLMs) optimized for
reasoning. Finetuned from Mistral-7B and CodeLlama-70B, Eurus models achieve
state-of-the-art results among open-source models on a diverse set of
benchmarks covering mathematics, code generation, and logical reasoning
problems. Notably, Eurus-70B beats GPT-3.5 Turbo in reasoning through a
comprehensive benchmarking across 12 tests covering five tasks, and achieves a
33.3% pass@1 accuracy on LeetCode and 32.6% on TheoremQA, two challenging
benchmarks, substantially outperforming existing open-source models by margins
more than 13.3%. The strong performance of Eurus can be primarily attributed to
UltraInteract, our newly-curated large-scale, high-quality alignment dataset
specifically designed for complex reasoning tasks. UltraInteract can be used in
both supervised fine-tuning and preference learning. For each instruction, it
includes a preference tree consisting of (1) reasoning chains with diverse
planning strategies in a unified format, (2) multi-turn interaction
trajectories with the environment and the critique, and (3) pairwise data to
facilitate preference learning. UltraInteract allows us to conduct an in-depth
exploration of preference learning for reasoning tasks. Our investigation
reveals that some well-established preference learning algorithms may be less
suitable for reasoning tasks compared to their effectiveness in general
conversations. Inspired by this, we derive a novel reward modeling objective
which, together with UltraInteract, leads to a strong reward model.