AMR-SD adds a reflection bottleneck to compress diagnostic signals into self-generated hints and uses asymmetric Causal Information Gain to create sparse token-level advantage signals, outperforming baselines and preventing late-stage collapse in RLVR.
<critique> In a guaranteed-victory analysis, each player’s remaining shot count must be treated as fully independent
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AMR-SD: Asymmetric Meta-Reflective Self-Distillation for Token-Level Credit Assignment
AMR-SD adds a reflection bottleneck to compress diagnostic signals into self-generated hints and uses asymmetric Causal Information Gain to create sparse token-level advantage signals, outperforming baselines and preventing late-stage collapse in RLVR.