Reasoning Model Distillation in Production: Trajectory Curation, Thinking-Token Formatting, Over-Thinking Mitigation, and Student RL Alignment
Reasoning Model Distillation in Production: Trajectory Curation, Thinking-Token Formatting, Over-Thinking Mitigation, and Student RL Alignment Distilling frontier reasoning models into compact language models has emerged as one of the most effective strategies for deploying low-latency, cost-efficient inference pipelines. Rather than training small models purely on input-output answer pairs, reasoning distillation transfers the intermediate exploration, backtracking, and verification trajectori
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