Zeng You, Yaofo Chen, Qiuwu Chen, Ying Sun, Shuhai Zhang, Yingjian Li, Yaowei Wang, Mingkui Tan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Recently, large language models (LLMs) have achieved significant progress in automated code generation. Despite their strong instruction-following capabilities, these models frequently struggled to align with user intent in the coding scenario. In particular, they were hampered by datasets that lacked diversity and failed to address specialized tasks or edge cases. Furthermore, challenges in supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) led to failures in generating precise, human-intent-aligned code. To tackle these challenges and improve the code generation performance for automated programming systems, we propose Feedback-driven Adaptive Long/short-term memory reinforced Coding OptimizatioN (i.e., FALCON). FALCON leverages long-term memory to retain and apply learned knowledge, short-term memory to incorporate immediate feedback, and meta-reinforcement learning with feedback rewards to address global-local bi-level optimization and enhance adaptability across diverse code generation tasks. Extensive experiments show that FALCON achieves state-of-the-art performance, outperforming other reinforcement learning methods by over 4.5% on MBPP and 6.1% on Humaneval, with the code publicly available. https://anonymous.4open.science/r/FALCON-3B64/README.md.