Training data attribution is increasingly used to filter, reweight, and prioritize rollouts in reinforcement learning with verifiable rewards (RLVR). However, these interventions often assume that individual samples, transitions, or trajectories can be attributed independently of the other data used in the same update. We show that this assumption is not merely a property of the attribution estimator, but a structural property of the training objective. We recast training data attribution as individual treatment effect estimation and use the Stable Unit Treatment Value Assumption (SUTVA) to determine when a proposed attribution unit is well-defined. This yields a simple criterion that attribution is only valid at the level where the algorithm assigns a stable contribution to that unit. Applying this criterion to RLVR, we show that the PPO objective admits trajectory-level attribution, whereas naive trajectory-only attribution is not well-defined for GRPO unless peer context is modeled. Experimental results support this distinction and provide preliminary evidence for group-level attribution under GRPO.

Traditional in-vehicle agents mainly follow an instruction-following paradigm, where actions are executed only after explicit user commands. However, real-world cabin scenarios often require agents to proactively determine whether intervention is necessary by jointly reasoning over visual and state inputs. To address this gap, we propose CabinProAct, a multimodal benchmark for proactive intelligent cabin agents. In this setting, agents must jointly reason over in-cabin and out-cabin visual information, vehicle states, and executable APIs to autonomously decide whether to intervene and generate corresponding actions. We further introduce an execution-verification-based synthetic data generation framework to automatically construct execution-consistent proactive interaction data. We evaluate six state-of-the-art multimodal models. The results indicate that execution-grounded synthetic supervision effectively transfers to real-world in-cabin settings, enabling more robust proactive responses in real-world cabin scenarios.