Browsing: reinforcement

@dataclass class MemoryItem: memory_id: int topic: str entity: str slot: str value: str text: str def build_memory_bank() -> List[MemoryItem]: entities = [ { “entity”: “Astra”, “topic”:…

series about Reinforcement Learning (RL), following Sutton and Barto’s famous book “Reinforcement Learning” [1]. In the previous posts we finished dissecting Part I of said book,…

, Reinforcement Learning — learning from observations and rewards — is the method most alike to the way humans (and animals) learn.  Despite this similarity, it also remains the most…

In the current landscape of generative AI, the ‘scaling laws’ have generally dictated that more parameters equal more intelligence. However, Liquid AI is challenging this convention…

NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting a ‘Rollout-as-a-Service’ philosophy, the system decouples…

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We…

ByteDance Seed recently dropped a research that might change how we build reasoning AI. For years, devs and AI researchers have struggled to ‘cold-start’ Large Language…

Kyutai has released Hibiki-Zero, a new model for simultaneous speech-to-speech translation (S2ST) and speech-to-text translation (S2TT). The system translates source speech into a target language in…

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment,…

on Real-World Problems is Hard Reinforcement learning looks straightforward in controlled settings: well-defined states, dense rewards, stationary dynamics, unlimited simulation. Most benchmark results are produced under…