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This study introduces section-aware compression for reasoning traces, training models to drop filler narration while preserving compute and verification spans, matching or exceeding original accuracy while using 2-3 times fewer tokens.
A researcher trained small language models on their own self-generated coding mistakes and corrections, achieving 80% on HumanEval and surpassing GPT-3.5 on math, demonstrating effective self-improvement with minimal resources.