Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing (Apr 2026)

Title: Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing (Apr 2026)
Link: http://arxiv.org/abs/2604.02288v1
Date: April 2026

Summary:
This paper introduces Sample-Routed Policy Optimization (SRPO), an on-policy framework for reinforcement learning with verifiable rewards (RLVR). SRPO addresses the stability and efficiency trade-offs between Group-Relative Policy Optimization (GRPO) and Self-Distillation Policy Optimization (SDPO) by routing correct samples to the reward-aligned GRPO branch and failed samples to the logit-level SDPO correction branch. It further utilizes an entropy-aware dynamic weighting mechanism to suppress unreliable signals, leading to superior performance in scientific reasoning and tool-use tasks.

Key Topics:
- Reinforcement Learning with Verifiable Rewards (RLVR)
- Group-Relative Policy Optimization (GRPO)
- Self-Distillation Policy Optimization (SDPO)
- Sample Routing
- Large Language Model Post-training
- Entropy-aware Dynamic Weighting
- Scientific Reasoning

Chapters:
00:00 - Introduction to RLVR
01:22 - Analyzing GRPO Stability
03:35 - SDPO Granular Feedback
04:52 - Solving Optimization Ambiguity
06:55 - Addressing Signal Degradation
08:20 - Building SRPO Architecture
10:00 - Advantage Mixing Limitations
11:35 - Implementing Dynamic Weighting
13:50 - Performance Benchmark Results
15:25 - Goldilocks Response Lengths
17:10 - Efficiency Compute Paradox
19:15 - Future Error Taxonomy

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