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Group-Calibrated On-Policy Distillation boosts long-context reasoning performance by reconciling teacher guidance with verifier feedback, achieving up to a 12-point increase in benchmark scores.
A simple data recipe can outperform complex reward engineering in enhancing long-context reasoning for large language models.
