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Physics in Next-token Prediction
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We discovered the underlying physics in Next-token Prediction (NTP). We identified the law of information conservation within NTP and proposed the First Law of Information Capacity (IC-1), demonstrating that the essence of intelligence emergence in auto-regressive models is fundamentally a process of information transfer. We also introduced Landauer's Principle into NTP, formulating the Second Law of Information Capacity (IC-2), which establishes the relationship between auto-regressive model training and energy consumption. Additionally, we presented several corollaries, which hold practical significance for production practices. Finally, we demonstrate the consistency between the Law of Information Capacity and the Scaling Law for Neural Language Models, the Knowledge Capacity Scaling Laws, and the Scaling Laws for Precision.
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Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection
Finetuning forgetting follows a multiplicative scaling law in model size, finetuning tokens, and injected pretraining fraction, with 1% injection nearly eliminating forgetting.
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