SRA reframes CTKD by aligning attention-weighted span centers of mass in a multi-particle system model with geometric regularization and span logit distillation, claiming consistent outperformance over baselines.
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Pith papers citing it
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2026 2verdicts
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ReCal introduces hierarchical reward decomposition and distribution-aware optimization to address ambiguous credit assignment and optimization bias in RL-based LLM routing.
citing papers explorer
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SRA: Span Representation Alignment for Large Language Model Distillation
SRA reframes CTKD by aligning attention-weighted span centers of mass in a multi-particle system model with geometric regularization and span logit distillation, claiming consistent outperformance over baselines.
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ReCal: Reward Calibration for RL-based LLM Routing
ReCal introduces hierarchical reward decomposition and distribution-aware optimization to address ambiguous credit assignment and optimization bias in RL-based LLM routing.