Browsing: Reinforcement

Training large language models requires accurate feedback signals, but traditional reinforcement learning (RL) often struggles with reward signal reliability. The quality of these signals directly influences…

Large language models (LLMs) now drive the most advanced conversational agents, creative tools, and decision-support systems. However, their raw output often contains inaccuracies, policy misalignments, or…

@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”:…

You can use reinforcement Fine-Tuning (RFT) in Amazon Bedrock to customize Amazon Nova and supported open source models by defining what “good” looks like—no large labeled…

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 December 2025, we announced the availability of Reinforcement fine-tuning (RFT) on Amazon Bedrock starting with support for Nova models. This was followed by extended support…

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…

Foundation models deliver impressive out-of-the-box performance for general tasks, but many organizations need models to consume their business knowledge. Model customization helps you bridge the gap…

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…