How Counterfactual Policy Optimization Boosts Visual Fidelity in Multimodal Reasoning (ICML 2026)
The paper introduces Counterfactual Policy Optimization (CFPO), a training‑time framework that inserts causal consistency constraints into multimodal reinforcement learning, forcing vision‑language models to rely on essential visual evidence and achieving consistent accuracy gains across real‑world and math‑centric benchmarks.
