BIRD

BIRD: Bootstrapped Iterative Self-Reasoning Distillation reduces LLM chain-of-thought length while improving accuracy

Researchers introduce BIRD (Bootstrapped Iterative Self-Reasoning Distillation), a two-stage method for compressing chain-of-thought reasoning in large language models without sacrificing accuracy. The approach first fine-tunes a model on brevity-instructed correct traces to warm-start the rollout distribution, then applies on-policy reverse-KL distillation against a concise self-teacher. On Qwen3-8B, BIRD improves MATH-500 accuracy from 86.2% to 92.0% while cutting average response length from 3,099 to 1,115 tokens, outperforming cold-start on-policy distillation baselines.