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On-policy self-distillation (OPSD) provides denser token-level supervision and better computational efficiency than Reinforcement Learning with Verifiable Rewards (RLVR). However, this denser supervision may introduce substantial noise and training instability. Existing improvements often rely on high-variance per-toke...
This work introduces MRCL, a Multimodal Reasoning Continual Learning benchmark, and proposes Continual Policy Optimization (CPO), a replay-free framework grounded in a prior-task behavioral KL objective that consistently reduces forgetting while preserving, and in some cases improving, pretrained model capabilities.
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