UniVG-R1: Reasoning Guided Universal Visual Grounding with...
Introducing OpenAI o3 and o4-mini | OpenAI
"For the first time, these models can integrate images directly into their chain of thought. They don’t just see an image—they think with it. This unlocks a new class of problem-solving that blends visual and textual reasoning, reflected in their state-of-the-art performance across multimodal benchmarks."
Bertsekas, D. P. (2019). Rollout, Policy Iteration, and Distributed Reinforcement Learning
(Any relation to economics or tariffs? Did look recently like a Gemini chat question came out of nowhere, possibly the result of agents on Android, however about marketing and seemingly inverted demographics like a sales projection. Maybe the future of ads somehow linked to reading list or Discover feed if conventional channels evaporate. Or this may have resulted from a partially typed prompt that was auto-completed.)
Richard S. Sutton - Wikipedia
Model-Based Transfer Learning for Contextual Reinforcement Learning
RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
View PDF
Training Language Models to Self-Correct via Reinforcement Learning
View PDF
Random robots are more reliable: New AI algorithm for robots consistently outperforms state-of-the-art systems
Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs
Pearl: A Production-ready Reinforcement Learning Agent
Download PDF
The third New England RLHF Hackers Hackathon
Chip Placement with Deep Reinforcement Learning
Using reinforcement learning for dynamic planning in open-ended conversations
An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains | Journal of Artificial Intelligence Research
Pre-training generalist agents using offline reinforcement learning
PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations
Quantization for Fast and Environmentally Sustainable Reinforcement Learning
Combining AI and computational science for better, faster, energy efficient predictions
Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents
Microsoft AI Research Introduces A New Reinforcement Learning Based Method, Called 'Dead-end Discovery' (DeD), To Identify the High-Risk States And Treatments In Healthcare Using Machine Learning