#Reinforcement Learning
45 articles with this tag
Agent-Designing Agents Emerge
Memento-Skills introduces an agent-designing agent that autonomously creates and refines specialized LLM agents through skill evolution, bypassing core LLM retraining.
OS-Themis: Scalable Rewards for GUI Agents
OS-Themis revolutionizes GUI agent training with a scalable, milestone-based critic framework and OGRBench, achieving significant performance uplifts.
OS-Themis: Scalable Rewards for Robust RL
OS-Themis, a new multi-agent critic framework, revolutionizes GUI agent training by providing scalable, accurate rewards through milestone decomposition and evidence auditing.
Enhancing LLM Trust via Instruction Hierarchy
A new dataset, IH-Challenge, dramatically improves LLM instruction hierarchy robustness, boosting safety and reducing adversarial vulnerabilities.
Databricks Buys Quotient AI
Databricks acquires Quotient AI to enhance AI agent reliability and performance in production environments, integrating its evaluation technology into key products.
Databricks' KARL Cuts Agent Costs
Databricks' new KARL AI agent drastically cuts costs and latency for enterprise knowledge tasks using custom reinforcement learning.
RLAIF: Unpacking the Latent Value Hypothesis
The latent value hypothesis explains RLAIF by positing that pretraining encodes human values as representation directions, activated by constitutional prompts.
RLAIF Explained: Latent Values in LLMs
RLAIF explained: Human values are latent directions in LLM representations, activated by constitutional prompts, with alignment ceiling tied to model capacity and data quality.
AI Governance: Optimization's Normative Limits
A new paper on arXiv argues that optimization-based AI, including RLHF LLMs, are formally incapable of normative governance due to inherent structural limitations.
AI Agents Learn to Cooperate Without Rules
Google researchers propose a simpler way for AI agents to cooperate: train them against diverse opponents, leveraging in-context learning to drive mutual cooperation through 'extortion' dynamics.
AI Learns Faster by Predicting the Future
AI learns faster with Predictive Inverse Dynamics Models (PIDMs) by forecasting future states, making imitation learning more data-efficient than traditional methods.
RL Fixes Overfitting in AI Radiology Reports
Microsoft Research’s UniRG framework uses reinforcement learning guided by clinical error signals to achieve state-of-the-art performance in AI radiology reports.
Argos Framework Delivers Grounded AI Reasoning
Argos is an agentic verification framework that fundamentally changes reinforcement learning by rewarding models only for Grounded AI reasoning based on verifiable evidence.
DeepMind, OpenAI Vets Launch humans& for Human-Centered AI
A new frontier AI lab, humans&, launched by veterans of DeepMind and OpenAI, aims to pivot the industry toward truly human-centered AI focused on collaboration and trust.
Uniqueness-Aware RL stops LLMs from getting lazy
Uniqueness-Aware RL prevents LLMs from converging on a single solution path by explicitly rewarding correct answers that employ rare problem-solving strategies.
Poolside’s Full-Stack Bet: Building AGI Agents from Data Centers to Code Completion
ChatGPT prompt injection is so bad they built an AI attacker
LLM Agent Reinforcement Learning Gets Practical
OpenAI Unveils Agent RFT: Revolutionizing AI with Self-Improving Tool-Using Models
AI Model Confessions: A New Honesty Layer
NVIDIA Autonomous Driving AI Gains Human-Like Reasoning
DR Tulu deep research: Open AI closes proprietary gap
AlphaProof system proves its worth at the Math Olympiad
OpenAI’s Agent RFT: Boosting Autonomous AI Performance Through Tailored Reinforcement Learning
Kimi Linear promises to beat full attention with less memory
Microsoft's ECHO Language Model learns from failure
Cognition's New Bet on Fast Context Retrieval
Reinforcement Fine-Tuning: Osmosis AI fine-tunes agents past FMs
LoRA vs full fine-tuning: The debate is over
New Fast GPU Weight Transfer Syncs Trillion-Parameter AI in 1.3s
Updating the brains of a massive AI model used to be a sluggish affair, often taking minutes to sync new knowledge from a training cluster to a live inference c...
New Fast GPU Weight Transfer Syncs Trillion-Parameter AI in 1.3s
Updating the brains of a massive AI model used to be a sluggish affair, often taking minutes to sync new knowledge from a training cluster to a live inference c...
OpenAI's Leap Towards Reasoning and Automated Discovery with GPT-5
AI's Dual Realities: Hallucinations, Augmentation, and the Micro-Model Frontier
Reinforcement Fine-Tuning: Elevating AI Reasoning with Grader-Driven Optimization
OpenAI's Hallucination Breakthrough: A Feature, Not a Bug, and How to Fix It
OpenAI’s Open-Weight GPT-OSS Challenges AI Landscape
OpenAI’s AI Achieves Gold at International Math Olympiad, Unveiling Path to General Reasoning
AI's Predictable Ascent: Scaling Laws Reshape the Path to Human-Level Intelligence
DeepSeek's Reasoning Leap Reshapes AI Scaling Paradigms
OpenAI’s New ChatGPT Agent Unifies AI Capabilities
CollabLLM: Microsoft Boosts LLM AI Collaboration
Waymo's AI Shift to Generative Learning for Autonomous Adaptation
Mistral AI's New Reasoning LLMs and Over $1 Billion in Funding
Aampe Has $18M to Deploy 100 Million AI Agents with Reinforcement Learning
<p>Their agents are managing on the order of 15-200 billion decisions every week that determine product surface interactions.</p><p>Each AI agent learns and adapts in real time, helping their user manage their attention and make complex choices in a world of material and content abundance.</p>
