Towards Better RL Training Data Utilization via Second-Order Rollout
arXiv:2602.22765v1 Announce Type: new Abstract: Reinforcement Learning (RL) has empowered Large Language Models (LLMs) with strong reasoning capabilities, but vanilla RL mainly focuses on generation capability improvement by training with only first-order rollout (generating multiple responses for a question), and we argue that this approach fails to fully exploit the potential of training data because of the neglect of critique capability training. To tackle this problem, we further introduce the concept of second-order rollout (generating multiple critiques for a response) and propose a unified framework for jointly training generation and critique capabilities. Extensive experiments across various models and datasets demonstrate that our approach can utilize training data more effectively than vanilla RL and achieve better performance under the same training data. Additionally, we uncover several insightful findings regarding second-order rollout and critique training, such as the
arXiv:2602.22765v1 Announce Type: new Abstract: Reinforcement Learning (RL) has empowered Large Language Models (LLMs) with strong reasoning capabilities, but vanilla RL mainly focuses on generation capability improvement by training with only first-order rollout (generating multiple responses for a question), and we argue that this approach fails to fully exploit the potential of training data because of the neglect of critique capability training. To tackle this problem, we further introduce the concept of second-order rollout (generating multiple critiques for a response) and propose a unified framework for jointly training generation and critique capabilities. Extensive experiments across various models and datasets demonstrate that our approach can utilize training data more effectively than vanilla RL and achieve better performance under the same training data. Additionally, we uncover several insightful findings regarding second-order rollout and critique training, such as the importance of label balance in critique training and the noise problem of outcome-based rewards, which can be mitigated through sampling techniques. Our work offers a preliminary exploration of dynamic data augmentation and joint generation-critique training in RL, providing meaningful inspiration for the further advancement of RL training
Executive Summary
This article proposes a novel approach to Reinforcement Learning (RL) training data utilization, dubbed second-order rollout, which generates multiple critiques for a response in addition to the traditional first-order rollout of generating multiple responses for a question. The authors argue that this approach can more effectively exploit the potential of training data and achieve better performance under the same training data. The unified framework for jointly training generation and critique capabilities is evaluated through extensive experiments across various models and datasets, yielding several insightful findings. This work offers a preliminary exploration of dynamic data augmentation and joint generation-critique training in RL, providing meaningful inspiration for further advancements in RL training.
Key Points
- ▸ Second-order rollout generates multiple critiques for a response, in addition to the traditional first-order rollout of generating multiple responses for a question.
- ▸ The approach can more effectively exploit the potential of training data and achieve better performance under the same training data.
- ▸ The unified framework for jointly training generation and critique capabilities is proposed, and extensive experiments are conducted across various models and datasets.
Merits
Strength in Addressing Critique Capability Training
The authors address a significant limitation of vanilla RL by introducing second-order rollout, which enables critique capability training and more effective utilization of training data.
Demerits
Limitation in Handling Label Balance
The authors acknowledge the importance of label balance in critique training but do not provide a comprehensive solution, leaving room for further research.
Limitation in Addressing Noise Problem
The authors identify the noise problem of outcome-based rewards but propose a mitigation technique through sampling, which may not be scalable for large datasets.
Expert Commentary
The article presents a significant contribution to the field of RL training data utilization, offering a novel approach to joint generation-critique training. While there are limitations to the proposed approach, the authors provide a comprehensive analysis of the strengths and weaknesses, setting the stage for further research and development. The implications of this work are far-reaching, with potential applications in natural language processing, dialogue systems, and other areas of RL research.
Recommendations
- ✓ Future research should focus on developing more scalable and efficient techniques for addressing the noise problem of outcome-based rewards.
- ✓ The proposed approach should be applied to real-world RL problems to further validate its effectiveness and identify potential limitations.
