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

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 coding scenarios. 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 is structured into two hierarchical levels. From the global level, long-term memory improves code quality by retaining and applying learned knowledge. At the local level, short-term memory allows for the incorporation of immediate feedback from compilers and AI systems. Additionally, we introduce meta-reinforcement learning with feedback rewards to solve the global-local bi-level optimization problem and enhance the model's adaptability across diverse code generation tasks. Extensive experiments demonstrate that our technique achieves state-of-the-art performance, leading other reinforcement learning methods by more than 4.5 percentage points on the MBPP benchmark and 6.1 percentage points on the Humaneval benchmark. The open-sourced code is publicly available at https://github.com/titurte/FALCON.

Structure-Preserving Transformers for Learning Parametrized Hamiltonian Systems

Two of the many trends in neural network research of the past few years have been (i) the learning of dynamical systems, especially with recurrent neural networks such as long short-term memory networks (LSTMs) and (ii) the introduction of transformer neural networks for natural language processing (NLP) tasks. Both of these trends have created enormous amounts of traction, particularly the second one: transformer networks now dominate the field of NLP. Even though some work has been performed on the intersection of these two trends, this work was largely limited to using the vanilla transformer directly without adjusting its architecture for the setting of a physical system. In this work we use a transformer-inspired neural network to learn a complicated non-linear dynamical system and furthermore (for the first time) imbue it with structure-preserving properties to improve long-term stability. This is shown to be extremely important when applying the neural network to real world applications.