Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing
Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing explores A unified reinforcement learning framework for large language models that improves training stability and efficiency by intelligently routing samples to different optimization strategies.. Commercial viability score: 7/10 in LLM Training.
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