Interpreting Reinforcement Learning Agents with Susceptibilities

Researchers have extended susceptibilities, a neural network interpretability technique, into reinforcement learning by measuring how agent behavior responds to loss perturbations during training. The work demonstrates that this lens captures internal developmental patterns invisible in policy analysis alone, validated through activation steering experiments. The framework's applicability to RLHF post-training suggests a pathway for interpreting how reward signals shape model internals, addressing a critical gap in RL transparency as these systems scale into production deployment.

Modelwire context

Skeptical read

The authors don't disclose what statistical assumptions underpin their causal claims about how loss perturbations map to agent behavior changes. Activation steering is presented as validation, but steering success doesn't prove the susceptibility measurements are actually identifying causal mediators rather than correlates.

This lands one day after an arXiv audit of mechanistic interpretability research found a systematic pattern: papers invoke causal language (circuits, mediators, abstraction) without stating the identification assumptions required to support those claims. The susceptibilities work follows the same pattern. It measures how agent outputs respond to loss tweaks and uses steering to validate, but never explicitly states whether it's assuming linear causal structure, temporal precedence, or absence of confounders. The audit flagged exactly this gap across 30 papers, and this appears to be another instance.

If the authors release a follow-up paper or appendix within six months that explicitly states their identification assumptions (e.g., 'we assume no hidden confounders between loss perturbations and behavioral changes'), that signals they took the audit seriously. If not, treat the causal framing as unvalidated correlation.

Coverage we drew on

Position: Mechanistic Interpretability Must Disclose Identification Assumptions for Causal Claims · arXiv cs.LG

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Mentionssusceptibilities · reinforcement learning · RLHF · activation steering

Read full story at arXiv cs.LG →(arxiv.org)

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