WS-GRPO: Weakly-Supervised Group-Relative Policy Optimization for Rollout-Efficient Reasoning
arXiv:2602.17025v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is effective for training language models on complex reasoning. However, since the objective is defined relative to a group of sampled trajectories, extended deliberation can create more chances to realize relative gains, leading to inefficient reasoning and overthinking, and complicating the trade-off between correctness and rollout efficiency. Controlling this behavior is difficult in practice, considering (i) Length penalties are hard to calibrate because longer rollouts may reflect harder problems that require longer reasoning, penalizing tokens risks truncating useful reasoning along with redundant continuation; and (ii) supervision that directly indicates when to continue or stop is typically unavailable beyond final answer correctness. We propose Weakly Supervised GRPO (WS-GRPO), which improves rollout efficiency by converting terminal rewards into correctness-aware guidance over partial t
arXiv:2602.17025v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is effective for training language models on complex reasoning. However, since the objective is defined relative to a group of sampled trajectories, extended deliberation can create more chances to realize relative gains, leading to inefficient reasoning and overthinking, and complicating the trade-off between correctness and rollout efficiency. Controlling this behavior is difficult in practice, considering (i) Length penalties are hard to calibrate because longer rollouts may reflect harder problems that require longer reasoning, penalizing tokens risks truncating useful reasoning along with redundant continuation; and (ii) supervision that directly indicates when to continue or stop is typically unavailable beyond final answer correctness. We propose Weakly Supervised GRPO (WS-GRPO), which improves rollout efficiency by converting terminal rewards into correctness-aware guidance over partial trajectories. Unlike global length penalties that are hard to calibrate, WS-GRPO trains a preference model from outcome-only correctness to produce prefix-level signals that indicate when additional continuation is beneficial. Thus, WS-GRPO supplies outcome-derived continue/stop guidance, reducing redundant deliberation while maintaining accuracy. We provide theoretical results and empirically show on reasoning benchmarks that WS-GRPO substantially reduces rollout length while remaining competitive with GRPO baselines.
Executive Summary
The article introduces WS-GRPO, a novel approach to improve rollout efficiency in Group Relative Policy Optimization (GRPO) for training language models. WS-GRPO converts terminal rewards into correctness-aware guidance over partial trajectories, reducing redundant deliberation while maintaining accuracy. This method provides a more efficient and effective way to train language models on complex reasoning tasks, addressing the limitations of traditional GRPO methods. The authors demonstrate the effectiveness of WS-GRPO through theoretical results and empirical evaluations on reasoning benchmarks, showcasing its potential to improve rollout efficiency without compromising accuracy.
Key Points
- ▸ WS-GRPO improves rollout efficiency in GRPO for training language models
- ▸ Converts terminal rewards into correctness-aware guidance over partial trajectories
- ▸ Reduces redundant deliberation while maintaining accuracy
Merits
Improved Efficiency
WS-GRPO reduces rollout length while remaining competitive with GRPO baselines, making it a more efficient approach for training language models.
Outcome-Derived Guidance
WS-GRPO provides outcome-derived continue/stop guidance, reducing redundant deliberation and improving the overall performance of the model.
Demerits
Limited Calibration
The method may still require calibration of hyperparameters, which can be challenging in practice, particularly in complex reasoning tasks.
Expert Commentary
The introduction of WS-GRPO marks a significant advancement in the field of natural language processing, particularly in the context of complex reasoning tasks. By providing a more efficient and effective approach to training language models, WS-GRPO has the potential to improve the performance of various NLP applications. However, further research is needed to fully explore the capabilities and limitations of this method, as well as its potential applications in real-world scenarios. The article's emphasis on outcome-derived guidance and rollout efficiency highlights the importance of developing more innovative approaches to language model training, which can lead to breakthroughs in areas such as question answering, text generation, and conversational AI.
Recommendations
- ✓ Further research should be conducted to explore the applications of WS-GRPO in various NLP tasks and domains.
- ✓ The development of more advanced guidance mechanisms and rollout strategies should be prioritized to improve the efficiency and accuracy of language models.
