Get RICH or Die Scaling: Profitably Trading Inference Compute for Robustness

Abstract: Models are susceptible to adversarially out-of-distribution (OOD) data despite large training-compute investments into their robustification. Zaremba et al. (2025) make progress on this problem at test time, showing LLM reasoning improves satisfaction of model specifications designed to thwart attacks, resulting in a correlation between reasoning effort and robustness to jailbreaks. However, this benefit of test compute fades when attackers are given access to gradients or multimodal inputs. We address this gap, clarifying that inference-compute offers benefits even in such cases. Our approach argues that compositional generalization, through which OOD data is understandable via its in-distribution (ID) components, enables adherence to defensive specifications on adversarially OOD inputs. Namely, we posit the Robustness from Inference Compute Hypothesis (RICH): inference-compute defenses profit as the model's training data better reflects the attacked data's components. We empirically support this hypothesis across vision LLM and attack types, finding robustness gains from test-time compute if specification following on OOD data is unlocked by compositional generalization, while RL finetuning and protracted reasoning are not critical. For example, increasing emphasis on defensive specifications via prompting lowers the success rate of gradient-based multimodal attacks on VLMs robustified by adversarial pretraining, but this same intervention provides no such benefit to not-robustified models. This correlation of inference-compute's robustness benefit with base model robustness is the rich-get-richer dynamic of the RICH: attacked data components are more ID for robustified models, aiding compositional generalization to OOD data. Accordingly, we advise layering train-time and test-time defenses to obtain their synergistic benefit.