之前的工作大部分都在研究怎么迭代偏好优化模型的instruction tuning ability,这篇论文在讨论怎么通过preference optimization来优化模型的reasoning ability。作者提出了一种通过优化竞争性生成的Chain-of-Thought(CoT)候选人之间的偏好来提高推理任务性能的迭代方法。该方法通过优化导
2.于是我用deepseek r1 + rule reward正确的作为正例,采样模型本身错误的回答作为负例,去做dpo: 效果不错啊,而且居然难得的练dpo时的chosen reward没有降低,小刀剌屁股开了眼了,训练完我在测试集上测了一版,准确率也提升了很多,但是有限,由原来的50%准确率提升到70%,这也还不够啊,那咋整,ppo暂时练不起,...
posterior preference articulation approachOne of the most important issues in multiple response surface optimization (MRSO) is obtaining a satisfactory "compromise" solution considering a decision maker (DM)'s preference information on the tradeoffs among multiple responses. A promising alternative to ...
Large language models (LLMs) demonstrate impressive performance but lack the flexibility to adapt to human preferences quickly without retraining. In this work, we introduce Test-time Preference Optimization (TPO), a framework that aligns LLM outputs with human preferences during inference, removing ...
By combining odds ratio preference optimization (ORPO), we fine-tune and align SLMs using positive and negative signals generated by themselves. Additionally, we introduce process supervision for rewards in preference alignment by sampling-based inference simulation and process reward models. Compared ...
Practical, Theoretical or Mathematical/ case-based reasoningheuristic programmingiterative methodsknowledge acquisitionlearning (artificial intelligence)optimisationresource allocationscheduling/ CABINSknowledge acquisitioniterative revisionPractical scheduling problems generally require allocation of resources in the presence...
偏好优化与迭代更新:采用Direct Preference Optimization(DPO)方法优化模型策略。与传统RLHF方法不同,DPO直接基于偏好数据优化模型,无需额外训练奖励模型。同时,MCTS生成的新数据用于迭代地改进模型,形成一个动态的在线学习框架。 5. 本文贡献 本文的主要贡献包括以下几点: ...
整体上实验都是在llama上做的,因此对于其它model的有效性未知。 另外,实际模型在部署应用时,除非是像deepseek-R1那样需要long reasoning的任务,否则用户不能接受refine两轮之后再给结果。如此看来,利用数据做SFT将knowledge固化在模型参数中,仍然是第一优选。
[3] RLHF Workflow:From Reward Modeling to Online RLHF [4] BacktoBasics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs [5]DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models [6] INF’s Open-Source Large Language Models...