The last decade has seen vast improvements in humanoid robots, but graduating to widespread use might require going back to the fundamentals. “Not reliably,” Hurst said. “I don’t think it’s totally ...

An AI agent being trained through reinforcement learning on cloud-hosted GPUs reportedly opened a reverse connection to an external server, and researchers say it showed traffic patterns consistent ...

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

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 ...

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 ...

Oracle-based quantum algorithms cannot use deep loops because quantum states exist only as mathematical amplitudes in Hilbert ...

MIT researchers unveil a new fine-tuning method that lets enterprises consolidate their "model zoos" into a single, continuously learning agent.

In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...