Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing
Learn planning-based reasoning via trajectory collection and process reward synthesis, 7B model surpasses GPT-3.5-Turbo.
Key Findings
Methodology
This paper proposes a framework for learning planning-based reasoning through Direct Preference Optimization (DPO). The method optimizes reasoning by collecting trajectories and ranking them according to synthesized process rewards. Core components include trajectory collection, process reward synthesis, and DPO optimization.
Key Results
- On the LogiQA-v2 dataset, the 7B model achieved higher accuracy than GPT-3.5-Turbo, with a 7.0% improvement.
- On the ReClor dataset, the model showed a 2.5% in-domain improvement and a 3.0% cross-domain improvement.
- Through DPO and pDPO methods, the model excelled in both logical and mathematical reasoning tasks.
Significance
This research improves the reliability and accuracy of large language models in complex reasoning tasks by synthesizing process rewards. It addresses high latency and annotation costs in traditional methods, impacting both academia and industry significantly.
Technical Contribution
The technical contribution lies in proposing a new trajectory-based reasoning learning framework that combines offline simulation and Direct Preference Optimization, significantly enhancing reasoning efficiency and accuracy.
Novelty
This study is the first to combine synthesized process rewards with Direct Preference Optimization for reasoning learning in large language models, reducing reliance on human annotations.
Limitations
- In some complex reasoning scenarios, the model may still make reasoning errors, especially when the initial policy model's capability is limited.
- Synthesized process rewards may introduce noise, affecting the model's final performance.
Future Work
Future research directions include further optimizing the process reward model, exploring more types of reasoning tasks, and validating the method on larger datasets.
AI Executive Summary
Large language models have recently shown great potential in handling complex reasoning tasks, but concerns about hallucinations and errors in their reasoning processes have arisen. Existing methods either model reasoning as planning or rely on human annotations for process supervision, often leading to high latency and costs.
This paper proposes a new framework for learning planning-based reasoning through trajectory collection and process reward synthesis. The method optimizes collected trajectories using Direct Preference Optimization (DPO), significantly improving reasoning reliability and accuracy. Experimental results show that the 7B model surpasses strong counterparts like GPT-3.5-Turbo on challenging logical reasoning benchmarks such as LogiQA-v2.
The significance of this study lies in reducing reliance on human annotations, lowering latency and costs in the reasoning process, and providing a new approach for reasoning learning in large-scale language models. However, the model may still make reasoning errors in some complex scenarios. Future research will continue to optimize the process reward model and validate the method on larger datasets.
Deep Analysis
Background
Natural language reasoning is a fundamental component of AI development, with recent advancements in large language models achieving significant progress. However, these models often generate misleading reasoning processes in complex tasks, affecting their reliability in practical applications.
Core Problem
Large language models often exhibit hallucinations and errors during reasoning, especially in complex scenarios. These issues not only affect model accuracy but also lead to high latency and costs, limiting their widespread use in practical applications.
Innovation
The innovation of this paper lies in proposing a new trajectory-based reasoning learning framework that improves reasoning reliability by synthesizing process rewards and using Direct Preference Optimization. Compared to traditional methods, this approach significantly reduces reliance on human annotations.
Methodology
- �� Trajectory Collection: Collect initial reasoning trajectories from large language models.
- �� Process Reward Synthesis: Estimate expected rewards for intermediate states through offline simulation.
- �� Direct Preference Optimization: Optimize the policy model using synthesized process rewards.
Experiments
Experiments were conducted on logical reasoning benchmarks such as LogiQA-v2 and ReClor, using baselines like GPT-3.5-Turbo and Llama2-7B. Key hyperparameters include the number of trajectory samples and training details of the process reward model.
Results
Experimental results show that the 7B model achieved a 7.0% accuracy improvement on the LogiQA-v2 dataset and significant improvements on the ReClor dataset, validating the method's effectiveness.
Applications
This method can be applied in scenarios requiring complex reasoning capabilities, such as automated question-answering systems and intelligent assistants, significantly improving their reasoning accuracy and efficiency.
Limitations & Outlook
Despite its effectiveness, the method may still result in reasoning errors in some complex scenarios. Additionally, synthesized process rewards may introduce noise, affecting the model's final performance.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. You have a recipe (reasoning process) that you need to follow step by step to make a delicious dish (correct answer). Sometimes, you might misunderstand a step (reasoning error), leading to a dish that doesn't turn out as expected. This method is like a smart assistant that helps you optimize each step, ensuring you end up with a delicious dish.
ELI14 Explained like you're 14
Imagine you're playing a puzzle game. You need to solve puzzles step by step to win. Large language models are like your game assistant, but sometimes they give wrong hints, leading you astray. This method is like a super assistant that helps you optimize each choice, ensuring you win the game smoothly!
Glossary
Direct Preference Optimization
A method to optimize policy models by comparing preferences of different trajectories.
Used to optimize the policy model for reasoning processes.
Process Reward Model
A model used to evaluate each intermediate state in the reasoning process.
Used to synthesize process rewards and optimize the policy model.
Trajectory
A sequence of states and actions in the reasoning process.
Used for collecting and optimizing reasoning processes.
Monte Carlo Tree Search
A search algorithm used to optimize decision-making processes.
Used for planning and optimizing reasoning processes.
Logical Reasoning
The process of deriving conclusions through logical steps.
The main research focus of this paper.
Open Questions Unanswered questions from this research
- 1 How to validate the method's effectiveness on larger datasets?
- 2 How to address the noise issue in synthesized process rewards?
- 3 How to further reduce reliance on human annotations?
Applications
Immediate Applications
Intelligent Question-Answering Systems
Improve accuracy and efficiency of QA systems by optimizing reasoning processes.
Long-term Vision
General Artificial Intelligence
Advance the development of general AI by continuously optimizing reasoning capabilities.
Abstract
Large Language Models (LLMs) have demonstrated significant potential in handling complex reasoning tasks through step-by-step rationale generation. However, recent studies have raised concerns regarding the hallucination and flaws in their reasoning process. Substantial efforts are being made to improve the reliability and faithfulness of the generated rationales. Some approaches model reasoning as planning, while others focus on annotating for process supervision. Nevertheless, the planning-based search process often results in high latency due to the frequent assessment of intermediate reasoning states and the extensive exploration space. Additionally, supervising the reasoning process with human annotation is costly and challenging to scale for LLM training. To address these issues, in this paper, we propose a framework to learn planning-based reasoning through Direct Preference Optimization (DPO) on collected trajectories, which are ranked according to synthesized process rewards. Our results on challenging logical reasoning benchmarks demonstrate the effectiveness of our learning framework, showing that our 7B model can surpass the strong counterparts like GPT-3.5-Turbo.