Update README.md

README.md

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 # FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning
 
-FlowR2A is a generative multimodal driving planner that learns the **reward-conditioned action distribution** $p(a \mid r)$ with **flow matching**. Instead of treating simulation-based rewards as *discriminative targets* (as in scoring-based planners), FlowR2A reframes them as *generative conditions*, unifying the dense supervision of scoring-based methods with the dynamic proposal generation of anchor-based methods in a single model. This forces the planner to internalize how an action relates to its outcomes in safety, progress, comfort, and rule compliance.
+FlowR2A is a generative multimodal driving planner that learns the **reward-conditioned action distribution** p(a|r) with **flow matching**. Instead of treating simulation-based rewards as *discriminative targets* (as in scoring-based planners), FlowR2A reframes them as *generative conditions*, unifying the dense supervision of scoring-based methods with the dynamic proposal generation of anchor-based methods in a single model. This forces the planner to internalize how an action relates to its outcomes in safety, progress, comfort, and rule compliance.
 
 - 📄 **Paper:** FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning
 - 🌐 **Project page:** https://lixirui142.github.io/flowr2a-project-page/