Preeti
Large Language Models (LLMs) have shown impressive capabilities in handling various reasoning tasks expressed in natural language, such as math word problems, code generation, and planning. However, as the complexity of reasoning tasks increases, advanced LLMs struggle with errors and inconsistencies due to their auto-regressive nature. To address this challenge, researchers have developed the Q framework, which enhances LLMs’ multi-step reasoning abilities through deliberative planning. Q formalizes LLM reasoning as a Markov Decision Process (MDP) and introduces methods for estimating optimal Q-values of state-action pairs, guiding LLMs to select the most promising next steps efficiently within an A* search framework.
Several techniques have been explored to improve LLMs in complex reasoning tasks, including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and methods like Tree-of-Thoughts (ToT) and A search for planning capabilities. Q has demonstrated significant performance improvements across various reasoning tasks, outperforming traditional methods and closed-source models in math reasoning and code generation. The framework uses a versatile Q-value model trained solely on ground-truth data, making it adaptable to various tasks without modifications. By employing plug-and-play Q-value models as heuristic functions, Q* guides LLMs effectively without task-specific fine-tuning, showcasing its agility and superior performance in enhancing LLMs’ problem-solving capabilities significantly.
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Source link: https://www.marktechpost.com/2024/06/27/q-a-versatile-artificial-intelligence-ai-approach-to-improve-llm-performance-in-reasoning-tasks/?amp
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