Advancing Large Language Model Reasoning Capabilities with Preference Trees

핵심 개념

EURUS, a suite of large language models optimized for reasoning, achieves state-of-the-art results on diverse benchmarks covering mathematics, code generation, and logical reasoning problems by leveraging ULTRAINTERACT, a newly-curated large-scale, high-quality alignment dataset designed for complex reasoning tasks.

초록

The content introduces EURUS, a suite of large language models (LLMs) optimized for reasoning. EURUS models are finetuned from Mistral-7B and CodeLlama-70B and achieve state-of-the-art results on a diverse set of benchmarks covering mathematics, code generation, and logical reasoning problems.

The key to EURUS's strong performance is ULTRAINTERACT, a newly-curated large-scale, high-quality alignment dataset designed specifically for complex reasoning tasks. ULTRAINTERACT includes:

1. Diverse planning strategies in a unified format, such as sequential processing and tool creation, to provide diverse reasoning trajectories.
2. Multi-turn interaction trajectories with the environment and critique to improve models' capabilities to learn from feedback and correct previous errors.
3. Paired correct and incorrect actions organized in tree structures to facilitate preference learning.

ULTRAINTERACT can be used in both supervised fine-tuning and preference learning. Experiments show that using ULTRAINTERACT along with established datasets in instruction fine-tuning already achieves strong performance. ULTRAINTERACT further facilitates preference learning for reasoning tasks, improving the performance even further with KTO and NCA. Surprisingly, DPO hurts the performance, which is analyzed in-depth.

The content also introduces EURUS-RM-7B, a reward model trained on ULTRAINTERACT that demonstrates especially strong preference modeling performance on reasoning tasks, outperforming even GPT-4 on certain benchmarks.

통계

EURUS-70B beats GPT-3.5 Turbo in reasoning through a comprehensive benchmarking across 12 tests covering five tasks.
EURUS-70B 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%.

인용구

"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."
"ULTRAINTERACT can be used in both supervised fine-tuning and preference learning."
"Surprisingly, we observe that DPO hurts model performance on most benchmarks."

핵심 통찰 요약

Advancing LLM Reasoning Generalists with Preference Trees

by Lifan Yuan,G... 게시일 arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.02078.pdf

더 깊은 질문

How can the insights from ULTRAINTERACT and EURUS be applied to improve reasoning capabilities of other types of AI models beyond large language models

The insights gained from ULTRAINTERACT and EURUS can be instrumental in enhancing the reasoning capabilities of various AI models beyond just large language models. One key application is in the field of automated reasoning systems, where AI models are utilized to solve complex logical problems. By incorporating the preference tree structure and multi-turn interaction trajectories from ULTRAINTERACT, these reasoning systems can benefit from a more structured and comprehensive approach to problem-solving. The preference learning techniques employed in EURUS can also be adapted to train other AI models, enabling them to make more informed decisions based on feedback and critique. Additionally, the divide-and-conquer strategy used in ULTRAINTERACT can be applied to break down complex problems into smaller, more manageable tasks, improving the efficiency and accuracy of reasoning processes in various AI systems.

What are the potential limitations or drawbacks of the preference tree structure used in ULTRAINTERACT, and how could it be further improved or extended

While the preference tree structure in ULTRAINTERACT offers significant advantages in guiding AI models through reasoning tasks, there are potential limitations and areas for improvement. One drawback is the scalability of the preference tree approach, as the complexity of the tree may increase significantly with the number of turns and actions involved in a task. This could lead to challenges in managing and processing large amounts of data efficiently. To address this limitation, enhancements could be made to optimize the structure of the preference tree, such as introducing hierarchical organization or dynamic pruning mechanisms to focus on the most relevant actions and trajectories. Additionally, incorporating reinforcement learning techniques to adaptively adjust the preference tree based on model performance and feedback could further enhance its effectiveness in guiding AI systems through reasoning tasks.

Given the strong performance of EURUS-RM-7B on reasoning tasks, how could this reward model be leveraged to enhance the reasoning abilities of other language models or AI systems beyond just reranking

The success of EURUS-RM-7B in improving reasoning performance through reranking suggests several ways in which this reward model could be leveraged to enhance the reasoning abilities of other language models and AI systems. One key application is in fine-tuning existing language models with the reward model to prioritize correct responses and optimize decision-making processes. By incorporating the reward modeling objective from EURUS-RM-7B, AI systems can be trained to focus on increasing the rewards of chosen actions and decreasing those of rejected data, leading to more accurate and effective reasoning outcomes. Furthermore, the reward model could be integrated into reinforcement learning frameworks to guide AI systems in learning optimal strategies for reasoning tasks, enabling them to adapt and improve their performance over time. This approach could be particularly beneficial in domains where reasoning plays a critical role, such as in problem-solving, decision-making, and logical inference tasks.