Developer finds LLM training pitfalls: performance gains hide underlying issues

A developer encountered issues while training a 4-billion-parameter local LLM for the Eterna Clarity operating system. Despite achieving a perfect score on a benchmark, the model's performance degraded in other areas, highlighting the stability-plasticity problem where targeted improvements can inadvertently harm existing capabilities. The developer learned that any new behavior must be evaluated against retained behaviors, and that benchmarks become less effective once they influence the training data or selection process. AI

IMPACT Highlights the difficulty in ensuring LLM improvements are robust and don't degrade existing capabilities, emphasizing the need for careful evaluation.

RANK_REASON Developer's personal account of challenges in LLM training and evaluation.

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• 4-billion-parameter
• Eterna Clarity

• model release
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