🔁 Apprendre à un LLM français de 15M à penser plus profond — et à savoir quand s'arrêter 🇫🇷

In a pursuit to enhance the learning processes of a 15M parameter French language model, the author explored various strategies, ultimately finding that altering the model's computation form—by using a looped transformer architecture—yielded improvements in perplexity without adding parameters. The research detailed a journey of four initial failures when attempting to adjust learning dynamics, leading to the realization that the model's capacity, not its learning method, was the limiting factor. Successful strategies included the implementation of adaptive computation time and entropy-based stopping criteria during training, which improved in-domain coherence but highlighted weaknesses out-of-domain. Although these efforts resulted in more coherent outputs, they did not enhance the factuality due to the model's capacity limitations. The approach emphasized honest experimentation, highlighting both successes and failures, with the understanding that these preliminary findings require further validation through multi-seed evaluations.

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