Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing

TL;DR

This paper introduces SRPO, a unified reinforcement learning framework that combines the strengths of GRPO and SDPO, improving performance and stability in large language model training.

Contribution

The paper proposes Sample-Routed Policy Optimization (SRPO), a novel on-policy method that routes samples to different optimization strategies, enhancing stability and performance.

Findings

SRPO surpasses both GRPO and SDPO on five benchmarks.

SRPO improves average performance by 3.4% over GRPO and 6.3% over SDPO.

SRPO reduces per-step compute cost by up to 17.2%.

Abstract

Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models. While Group Relative Policy Optimization (GRPO) is widely adopted, its coarse credit assignment uniformly penalizes failed rollouts, lacking the token-level focus needed to efficiently address specific deviations. Self-Distillation Policy Optimization (SDPO) addresses this by providing denser, more targeted logit-level supervision that facilitates rapid early improvement, yet it frequently collapses during prolonged training. We trace this late-stage instability to two intrinsic flaws: self-distillation on already-correct samples introduces optimization ambiguity, and the self-teacher's signal reliability progressively degrades. To resolve these issues, we propose Sample-Routed Policy Optimization (SRPO), a unified on-policy framework that routes correct samples to GRPO's reward-aligned reinforcement and failed samples to SDPO's targeted logit-level correction. SRPO further incorporates an entropy-aware dynamic weighting mechanism to suppress high-entropy, unreliable distillation targets while emphasizing confident ones. Evaluated across five benchmarks and two model scales, SRPO achieves both the rapid early improvement of SDPO and the long-horizon stability of GRPO. It consistently surpasses the peak performance of both baselines, raising the five-benchmark average on Qwen3-8B by 3.4% over GRPO and 6.3% over SDPO, while simultaneously yielding moderate response lengths and lowering per-step compute cost by up to 17.2%.