Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower ...

Overview:  Python libraries help businesses build powerful tools for data analysis, AI systems, and automation faster and more efficiently.Popular librarie ...

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3 ...

Abstract: Repository-level code completion aims to generate code for unfinished code snippets within the context of a specified repository. Existing approaches mainly rely on retrievalaugmented ...

REC-R1 is a general framework that bridges generative large language models (LLMs) and recommendation systems via reinforcement learning. Check the paper here.

Abstract: Code optimization is a crucial task that aims to enhance code performance. However, this process is often tedious and complex, highlighting the necessity for automatic code optimization ...

Reinforcement Learning is at the core of building and improving frontier AI models and products. Yet most state-of-the-art RL methods learn primarily from outcomes: a scalar reward signal that says ...