Predictable behavior from scaling advanced AI systems is an extremely
desirable property. Although a well-established literature exists on how
pretraining performance scales, the literature on how particular downstream
capabilities scale is significantly muddier. In this work, we take a step back
and ask: why has predicting specific downstream capabilities with scale
remained elusive? While many factors are certainly responsible, we identify a
new factor that makes modeling scaling behavior on widely used multiple-choice
question-answering benchmarks challenging. Using five model families and twelve
well-established multiple-choice benchmarks, we show that downstream
performance is computed from negative log likelihoods via a sequence of
transformations that progressively degrade the statistical relationship between
performance and scale. We then reveal the mechanism causing this degradation:
downstream metrics require comparing the correct choice against a small number
of specific incorrect choices, meaning accurately predicting downstream
capabilities requires predicting not just how probability mass concentrates on
the correct choice with scale, but also how probability mass fluctuates on
specific incorrect choices with scale. We empirically study how probability
mass on the correct choice co-varies with probability mass on incorrect choices
with increasing compute, suggesting that scaling laws for incorrect choices
might be achievable. Our work also explains why pretraining scaling laws are
commonly regarded as more predictable than downstream capabilities and
contributes towards establishing scaling-predictable evaluations of frontier AI
models.

通过使用五个模型家族和十二个多项选择基准测试，我们展示了通过负对数似然性能计算的一系列转换逐渐降低了性能和规模之间的统计关系，同时揭示了导致这种降级的机制：下游度量要求将正确的选择与少量特定的错误选择进行比较，这意味着准确预测下游能力不仅要预测概率质量如何在正确选择上随规模集中，还要预测概率质量如何在特定错误选择上随规模波动。

为什么使用规模预测前沿的人工智能模型的下游能力如此困难？

Why Has Predicting Downstream Capabilities of Frontier AI Models with  Scale Remained Elusive?

Large language models such as GPT and Llama are trained with a next-token
prediction loss. In this work, we suggest that training language models to
predict multiple future tokens at once results in higher sample efficiency.
More specifically, at each position in the training corpus, we ask the model to
predict the following n tokens using n independent output heads, operating on
top of a shared model trunk. Considering multi-token prediction as an auxiliary
training task, we measure improved downstream capabilities with no overhead in
training time for both code and natural language models. The method is
increasingly useful for larger model sizes, and keeps its appeal when training
for multiple epochs. Gains are especially pronounced on generative benchmarks
like coding, where our models consistently outperform strong baselines by
several percentage points. Our 13B parameter models solves 12 % more problems
on HumanEval and 17 % more on MBPP than comparable next-token models.
Experiments on small algorithmic tasks demonstrate that multi-token prediction
is favorable for the development of induction heads and algorithmic reasoning
capabilities. As an additional benefit, models trained with 4-token prediction
are up to 3 times faster at inference, even with large batch sizes.

我们建议通过训练语言模型来预测多个未来标记，以提高样本利用效率，并对其下游能力进行改进，特别是在多词预测作为辅助训练任务时，在代码和自然语言生成模型方面获得了显著的改善。