April 14, 2025 report
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Over-training massive language fashions might make them tougher to fine-tune
A small group of AI researchers from Carnegie Mellon College, Stanford College, Harvard College and Princeton College, all within the U.S., has discovered that if massive language fashions are over-trained, it’d make them tougher to fine-tune. Of their paper posted on the arXiv preprint server, the group in contrast the influence of various quantities of coaching on a single LLM.
Over the previous couple of years, as AI researchers search to boost their merchandise to make them extra "clever," many have been pushed by the mantra that the extra coaching a mannequin is given, the higher the mannequin will probably be in the long run. On this new research, the analysis group has discovered some proof suggesting that there could also be some extent of diminishing returns with language mannequin coaching.
The researchers got here to this conclusion as they have been testing the return when coaching two totally different variations of the LLM OLMo-1B. Underneath one situation, they skilled it utilizing 2.3 trillion tokens, whereas within the different they used 3 trillion tokens. They then in contrast the situations by testing them with a number of benchmarks, equivalent to ARC and AlpacaEval. In so doing, they discovered that the mannequin skilled with extra tokens truly did worse when examined—as much as 3% worse.
Shocked by their findings, they ran extra checks and located related outcomes, suggesting that there’s some level at which extra coaching begins to make fashions much less "clever." The analysis group calls it "catastrophic overtraining," and suggests it is because of what they describe as "progressive sensitivity."
They additional recommend that because the variety of tokens rises, the extra fragile a mannequin turns into, which signifies that fine-tuning, which will be considered as including noise, begins to reverse the positive factors in enchancment that have been seen previous to the stress level.
To check their idea, they added Gaussian noise to among the fashions, and located that doing so led to the identical kind of efficiency degradation they’d witnessed earlier. They’ve named the purpose of no return, the "inflection level." After that time, they recommend, any additional coaching will scale back the steadiness of the mannequin, making it harder to tune in methods which are helpful for a desired set of functions.
The researchers conclude by suggesting that shifting ahead, builders of LLM fashions might need to make estimations relating to how a lot coaching is sufficient—or, discover different sorts of strategies that can enable for added coaching with a extra distant inflection level.
Extra data: Jacob Mitchell Springer et al, Overtrained Language Fashions Are Tougher to Effective-Tune, arXiv (2025). DOI: 10.48550/arxiv.2503.19206
Journal data: arXiv
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