BPPO: Binary Prefix Policy Optimization for Efficient GRPO-Style Reasoning RL with Concise Responses
- URL: http://arxiv.org/abs/2605.28028v1
- Date: Wed, 27 May 2026 06:34:17 GMT
- Title: BPPO: Binary Prefix Policy Optimization for Efficient GRPO-Style Reasoning RL with Concise Responses
- Abstract summary: We study whether all completions provide equally useful update signals in GRPO-style reasoning RL.<n>Our gradient-similarity analysis shows that, within the same prompt group, same-class completions often induce highly similar update directions.<n>We propose Binary Prefix Policy Optimization (BPPO), which uses the shortest correct completion and the shortest incorrect completion as a compact update unit.
- Score: 48.550535291129584
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Group Relative Policy Optimization (GRPO) is widely used for training reasoning models, but updating all sampled completions in each group incurs substantial cost and can reinforce verbose reasoning trajectories. In this paper, we study whether all completions provide equally useful update signals in GRPO-style reasoning RL. Our gradient-similarity analysis shows that, within the same prompt group, same-class completions often induce highly similar update directions, whereas correct-incorrect pairs provide more distinct contrastive signals. Motivated by this observation, we propose Binary Prefix Policy Optimization (BPPO), which uses the shortest correct completion and the shortest incorrect completion as a compact update unit while preserving full-group advantage normalization. BPPO further improves efficiency with adaptive completion scheduling and prefix-focused optimization; by updating only response prefixes, it avoids reinforcing redundant suffixes and encourages more concise responses. Experiments on GSM8K, MATH, and Geo3K show that BPPO achieves up to 6.08x speedup over GRPO while maintaining competitive accuracy, and reduces mean response length by approximately 30-50% without modifying the reward with an explicit length penalty.
Related papers
- Policy Improvement Reinforcement Learning [40.05196753615896]
Reinforcement Learning with Verifiable Rewards (RLVR) has become a central post-training paradigm for improving the reasoning capabilities of large language models.<n>Existing methods share a common blind spot: they optimize policies based on instantaneous group-level or batch-level statistics without ever verifying whether the resulting update actually improved the model.<n>We argue that the missing ingredient is policy improvement feedback: the ability to measure and optimize inter-iteration progress directly.
arXiv Detail & Related papers (2026-04-01T13:10:20Z) - Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization [60.87651283510059]
Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs.<n>We propose Dynamic Pruning Policy Optimization (DPPO), a framework that enables dynamic pruning while preserving unbiased gradient estimation.<n>To mitigate the data sparsity induced by pruning, we introduce Dense Prompt Packing, a window-based greedy strategy.
arXiv Detail & Related papers (2026-03-04T14:48:53Z) - iGRPO: Self-Feedback-Driven LLM Reasoning [88.83313431248473]
Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions.<n>We introduce Iterative Group Relative Policy Optimization (iGRPO), a two-stage extension of GRPO that adds dynamic self-conditioning through model-generated drafts.<n>Under matched rollout budgets, iGRPO consistently outperforms GRPO across base models.
arXiv Detail & Related papers (2026-02-09T18:45:11Z) - GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization [133.27496265096445]
We show how to apply Group Relative Policy Optimization under multi-reward setting without examining its suitability.<n>We then introduce Group reward-Decoupled Normalization Policy Optimization (GDPO), a new policy optimization method to resolve these issues.<n>GDPO consistently outperforms GRPO, demonstrating its effectiveness and generalizability for multi-reward reinforcement learning optimization.
arXiv Detail & Related papers (2026-01-08T18:59:24Z) - Anchoring Values in Temporal and Group Dimensions for Flow Matching Model Alignment [61.80228667422234]
VGPO redefines value estimation across both temporal and group dimensions.<n>It transforms the sparse terminal reward into dense, process-aware value estimates.<n>It replaces standard group normalization with a novel process enhanced by absolute values to maintain a stable optimization signal.
arXiv Detail & Related papers (2025-12-13T16:31:26Z) - Prefix Grouper: Efficient GRPO Training through Shared-Prefix Forward [10.640867597958863]
We propose Prefix Grouper, an efficient GRPO training algorithm that eliminates redundant prefixes via a Shared-Prefix Forward strategy.<n>By restructuring self-attention into two parts, our method enables the shared prefix to be encoded only once.<n>We provide both theoretical and empirical evidence that Prefix Grouper is training-equivalent to standard GRPO.
arXiv Detail & Related papers (2025-06-05T09:13:37Z) - CPPO: Accelerating the Training of Group Relative Policy Optimization-Based Reasoning Models [77.16976971950785]
This paper introduces Completion Pruning Policy Optimization (CPPO) to accelerate the training of reasoning models.<n>CPPO prunes completions with low absolute advantages, significantly reducing the number needed for gradient calculation and updates.<n> Experiments show that CPPO achieves up to $7.98times$ speedup on GSM8K and $3.48times$ on Math while preserving or even enhancing the accuracy compared to the original GRPO.
arXiv Detail & Related papers (2025-03-28T11:30:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.