Introspective X Training: Feedback Conditioning Improves Scaling Across all LLM Training Stages

In three linesIntrospective Training (IXT) uses a thinking reward model to annotate data with natural language feedback from pre-training onward. On 7.5-12B LLMs trained up to 18T tokens, the method improves compute efficiency by 2.8x and achieves performance levels unattainable otherwise in math and code domains.

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