Hacker News: Offline Reinforcement Learning for LLM Multi-Step Reasoning

Dec 23, 2024

—

Source URL: https://arxiv.org/abs/2412.16145
Source: Hacker News
Title: Offline Reinforcement Learning for LLM Multi-Step Reasoning

Feedly Summary: Comments

AI Summary and Description: Yes

Summary: The text discusses the development of a novel offline reinforcement learning method, OREO, aimed at improving the multi-step reasoning abilities of large language models (LLMs). This has significant implications in AI security and the overall utility of AI systems in complex tasks, making it relevant for professionals in AI and machine learning.

Detailed Description:
The paper outlines critical advancements in the application of reinforcement learning to enhance the reasoning capabilities of large language models (LLMs), which is pivotal for their deployment in multifaceted tasks. The main contributions and insights include:

– **Challenge Identification**:
– Existing methods like Direct Preference Optimization (DPO) face challenges in multi-step reasoning due to:
– Dependence on paired preference data, which is scarce for these tasks.
– Uniform treatment of all tokens, making effective credit assignment difficult in scenarios with sparse rewards.

– **Proposed Solution – OREO**:
– OREO (Offline Reasoning Optimization) aims to address these issues using offline reinforcement learning techniques.
– The method optimizes the soft Bellman Equation, allowing joint learning of a policy model and a value function.

– **Benefits of OREO**:
– Reduces the need for extensive pairwise data collection.
– Improves credit assignment processes in multi-step reasoning tasks.

– **Performance Evaluation**:
– Empirically shown to exceed existing offline learning methods across multiple benchmarks, specifically:
– Mathematical reasoning tasks (like GSM8K and MATH).
– Embodied agent control in environments such as ALFWorld.

– **Further Extensions**:
– OREO can be adapted to a multi-iteration approach with additional resources.
– The learned value function from OREO has the potential to enhance performance during test scenarios by guiding tree search processes without extra costs.

This research underscores advancements in AI, specifically in enhancing reasoning capabilities through innovative methods, which can have broader implications for AI security due to improved reliability of AI systems in critical applications. The performance benchmarks achieved also demonstrate the practical benefits of integrating advanced reinforcement learning approaches into AI models, further solidifying their role in infrastructure security and operational effectiveness in various domains.

1 2 4 5 a Act advancement advancements agent AI AI models Application applications Arch as benchmark benchmarks by C capabilities challenges control core cost Costs credit assignment critical critical applications cross D data data collection de demo deployment development e effective effectiveness end environment evaluation face for g Gen gs hack hacker Hacker News http HTTPS implications in infrastructure infrastructure security insights iOS ite k l language language model language models large large language model large language models learning led liability llm llms lm low mac machine Machine Learning making math mathematical reasoning model models multi news no o of off offline reinforcement learning on operation operational effectiveness optimization over performance performance benchmark performance benchmarks performance evaluation policy pre professionals R RCE reasoning reasoning abilities reasoning capabilities reasoning tasks reinforcement learning reliability research resources Role s search sec security Sig source SSE step reasoning system systems T Task tasks tech techniques test text the to token tokens TP US Valuation value function Wi x