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?

In Reinforcement Learning (RL), agents have no incentive to exhibit
predictable behaviors, and are often pushed (through e.g. policy entropy
regularization) to randomize their actions in favor of exploration. From a
human perspective, this makes RL agents hard to interpret and predict, and from
a safety perspective, even harder to formally verify. We propose a novel method
to induce predictable behavior in RL agents, referred to as
Predictability-Aware RL (PA-RL), which employs the state sequence entropy rate
as a predictability measure. We show how the entropy rate can be formulated as
an average reward objective, and since its entropy reward function is
policy-dependent, we introduce an action-dependent surrogate entropy enabling
the use of PG methods. We prove that deterministic policies minimizing the
average surrogate reward exist and also minimize the actual entropy rate, and
show how, given a learned dynamical model, we are able to approximate the value
function associated to the true entropy rate. Finally, we demonstrate the
effectiveness of the approach in RL tasks inspired by human-robot use-cases,
and show how it produces agents with more predictable behavior while achieving
near-optimal rewards.

提出一种名为可预测性感知强化学习 (Predictability-Aware RL，PA-RL) 的新方法，利用状态序列熵率作为可预测性度量，通过引入基于动作的替代熵来实现行为的可预测性，从而在人机互动场景的强化学习任务中产生更可预测的行为并实现接近最优奖励。