FALCON introduces an innovative feedback-driven reinforcement learning system that significantly improves LLM code generation quality by adapting to both immediate and long-term user feedback.

• Addresses LLMs' struggle to align with user intent in coding tasks
• Implements a dual memory architecture that processes both short and long-term feedback
• Outperforms existing systems by learning from specialized user requirements
• Demonstrates practical applications for software engineering workflows

This research matters for Engineering teams seeking to integrate AI coding assistants into development pipelines, offering more responsive and accurate code generation that adapts to specific project requirements rather than generic solutions.

FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization system