本文介绍了直接Q函数优化（DQO）方法，它通过将语言模型对齐问题转化为马尔可夫决策过程（MDP），并利用软演员-评论家（SAC）框架，有效解决了大型语言模型（LLM）对齐中的挑战。DQO引入过程奖励，支持多步推理，并能识别和优化部分正确的推理步骤。该方法无需在线采样，降低了计算成本，并能从不平衡数据中学习。实验结果表明，DQO在数学推理任务上显著优于其他基线方法，如DPO和DRO，尤其是在GSM8K和MATH数据集上取得了显著提升。

🎯DQO将LLM的响应生成过程视为马尔可夫决策过程（MDP），并采用软演员-评论家（SAC）框架，实现逐步学习。这与传统的单步优化方法不同，更适合处理需要多步推理的任务。

💡DQO引入过程奖励，为中间步骤提供反馈信号，这对于解决数学问题或生成复杂代码等任务至关重要。这种奖励机制有助于模型识别并优化正确的推理步骤，即使在部分正确的响应中也能进行改进。

🧪DQO通过λ-return机制平衡短期和长期奖励，并通过重要性采样减少训练数据和模型策略之间的分布偏移，从而增强了离线学习能力。这使得DQO无需在线采样，降低了计算成本，并提高了模型的鲁棒性。

📈实验结果表明，DQO在GSM8K和MATH数据集上的表现均优于其他基线方法，尤其是在使用过程奖励后，性能得到进一步提升。这证明了DQO在处理多步推理任务和对齐LLM方面的有效性。

Aligning large language models (LLMs) with human preferences is an essential task in artificial intelligence research. However, current reinforcement learning (RL) methods face notable challenges. Proximal Policy Optimization (PPO) and similar techniques often demand extensive online sampling, which can lead to high computational costs and instability. Offline RL methods like Direct Preference Optimization (DPO) avoid these issues but face difficulties with tasks requiring multi-step reasoning, such as solving mathematical problems or generating complex code. These methods frequently treat the generation process as a single-step problem, neglecting the long-horizon dependencies intrinsic to many reasoning tasks. Additionally, sparse reward functions, which provide feedback only at the conclusion of a reasoning sequence, make intermediate step guidance challenging.

Researchers from ByteDance and UCLA have introduced Direct Q-function Optimization (DQO) to address these challenges. DQO frames the response generation process as a Markov Decision Process (MDP) and utilizes the Soft Actor-Critic (SAC) framework. By parameterizing the Q-function directly through the language model, DQO shifts the LLM alignment problem into a structured, step-by-step learning process. Unlike bandit-based methods, DQO incorporates process rewards—intermediate feedback signals—to support multi-step reasoning more effectively.

A key feature of DQO is its ability to identify and optimize correct reasoning steps even within partially correct responses. For example, in mathematical problem-solving, DQO assigns higher value to accurate steps and penalizes errors, enabling incremental improvement in reasoning. This makes DQO particularly suitable for tasks requiring detailed, long-horizon decision-making.

Technical Implementation and Practical Advantages

DQO’s approach is centered on parameterizing the Q-function using the language model, thereby integrating policy and value functions. The model updates its Q-function and value function based on the Soft Bellman Equation. KL-regularization ensures stable learning and helps prevent overfitting to specific samples.

To handle challenges such as high bias in temporal difference errors, DQO employs λ-return, a mechanism that balances short-term and long-term rewards for more stable training. Importance sampling further enhances DQO’s offline learning capabilities by reducing distributional shifts between the training data and the model’s policy.

DQO offers several practical advantages. It eliminates the need for online sampling, reducing computational costs. Moreover, it can learn from unbalanced and negative samples, enhancing its robustness across various scenarios. The use of process rewards helps refine reasoning capabilities while improving alignment with task requirements.

Results and Insights

Experimental evaluations of DQO on mathematical reasoning datasets—GSM8K and MATH—demonstrate its effectiveness. On the GSM8K dataset, DQO improved performance from a baseline of 59.06% to 87.26% for greedy generation and from 53.30% to 84.69% for sampling-based generation. These results surpass other baseline methods, including DPO and DRO. Similarly, on the MATH dataset, DQO outperformed baselines, achieving improvements of 1.18% in sampling and 1.40% in greedy generation.

Enhancing DQO with process rewards further boosted performance, suggesting its potential to incorporate additional supervisory signals. These results underscore DQO’s capability to handle multi-step reasoning tasks effectively and align LLMs with complex objectives.

Conclusion

Direct Q-function Optimization (DQO) offers a thoughtful approach to reinforcement learning for LLM alignment. By framing response generation as an MDP and utilizing the SAC framework, DQO addresses the limitations of existing methods. Its ability to integrate process rewards, handle unbalanced data, and stabilize training through λ-return and importance sampling makes it a practical solution for tasks involving multi-step reasoning.

Future research could explore applying DQO to other domains, such as code generation and dialogue systems, where long-horizon decision-making is critical. As AI systems evolve to tackle increasingly complex challenges, methods like DQO will play an important role in enhancing the alignment and performance of language models.

---

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 60k+ ML SubReddit.

Trending: LG AI Research Releases EXAONE 3.5: Three Open-Source Bilingual Frontier AI-level Models Delivering Unmatched Instruction Following and Long Context Understanding for Global Leadership in Generative AI Excellence….

The post Revolutionizing LLM Alignment: A Deep Dive into Direct Q-Function Optimization appeared first on MarkTechPost.