Summary of Verifierq: Enhancing Llm Test Time Compute with Q-learning-based Verifiers, by Jianing Qi et al.
VerifierQ: Enhancing LLM Test Time Compute with Q-Learning-based Verifiers
by Jianing Qi, Hao Tang, Zhigang Zhu
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in test-time computation for Large Language Models (LLMs) have significantly improved their reasoning capabilities through the use of verifier models. This paper introduces VerifierQ, a novel approach that integrates Offline Q-learning into LLM verifier models to address three key challenges: handling utterance-level Markov Decision Processes (MDPs), managing large action spaces, and mitigating overestimation bias. VerifierQ modifies the Bellman update for bounded Q-values, incorporates Implicit Q-learning (IQL) for efficient action space management, and integrates Conservative Q-learning (CQL) for balanced Q-value estimation. This approach enables parallel Q-value computation, improving training efficiency. Experimental results on mathematical reasoning tasks demonstrate VerifierQ’s superior performance compared to traditional supervised fine-tuning approaches, with improvements in efficiency, accuracy, and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves the way Large Language Models (LLMs) make decisions by using a new approach called VerifierQ. LLMs are special types of artificial intelligence that can understand and generate human-like language. Traditionally, they rely on supervised learning to make predictions. This paper shows how VerifierQ can be used to improve their decision-making abilities. The authors address three key challenges in making this work: handling complex situations, managing a large number of possibilities, and avoiding overconfidence. They propose a new way of computing Q-values that is more efficient and accurate than previous methods. Experimental results show that VerifierQ outperforms traditional approaches on tasks such as math problem-solving. |
Keywords
» Artificial intelligence » Fine tuning » Supervised
