Reinforcement Learning

Language model alignment has become a pivotal technique in making language technologies more user-centric and effective across different languages. Traditionally, aligning these models to mirror human preferences requires extensive, language-specific data, which is not always available,...
In today's digital age, the efficiency and reliability of networks, whether they are telecommunications frameworks or urban traffic systems, are pivotal. Artificial Intelligence (AI) is crucial in enhancing these networks through predictive maintenance and advanced traffic...

Researchers at the University of Oxford Introduce Craftax: A Machine Learning Benchmark for Open-Ended Reinforcement Learning

Building and using appropriate benchmarks is a major driver of advancement in RL algorithms. For value-based deep RL algorithms, there's the Arcade Learning Environment;...

Researchers from CMU and Peking Introduces ‘DiffTOP’ that Uses Differentiable Trajectory Optimization to Generate the Policy Actions for Deep Reinforcement Learning and Imitation Learning

According to recent studies, a policy's depiction can significantly affect learning performance. Policy representations such as feed-forward neural networks, energy-based models, and diffusion have...

This AI Paper Introduces StepCoder: A Novel Reinforcement Learning Framework for Code Generation

Large language models (LLMs) are advancing the automation of computer code generation in artificial intelligence. These sophisticated models, trained on extensive datasets of programming...

UC Berkeley Researchers Introduce SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

In recent years, researchers in the field of robotic reinforcement learning (RL) have achieved significant progress, developing methods capable of handling complex image observations,...

Researchers from Université de Montréal and Princeton Tackle Memory and Credit Assignment in Reinforcement Learning: Transformers Enhance Memory but Face Long-term Credit Assignment Challenges

Reinforcement learning (RL) has witnessed significant strides in integrating Transformer architectures, which are known for their proficiency in handling long-term dependencies in data. This...

Meta AI Researchers Open-Source Pearl: A Production-Ready Reinforcement Learning AI Agent Library

Reinforcement Learning (RL) is a subfield of Machine Learning in which an agent takes suitable actions to maximize its rewards. In reinforcement learning, the...

Researchers at UC Berkeley Introduced RLIF: A Reinforcement Learning Method that Learns from Interventions in a Setting that Closely Resembles Interactive Imitation Learning

Researchers from UC Berkeley introduce an unexplored approach to learning-based control problems, integrating reinforcement learning (RL) with user intervention signals. Utilizing off-policy RL on...

This AI Research from MIT and Meta AI Unveils an Innovative and Affordable Controller for Advanced Real-Time In-Hand Object Reorientation in Robotics

Researchers from MIT and Meta AI have developed an object reorientation controller that can utilize a single depth camera to reorient diverse shapes of...

Revolutionizing Digital Art: Researchers at Seoul National University Introduce a Novel Approach to Collage Creation Using Reinforcement Learning

Artistic collage creation, a field deeply intertwined with human artistry, has sparked interest in artificial intelligence (AI). The challenge arises from the need to...

This AI Paper Introduces Φ-SO: A Physical Symbolic Optimization Framework that Uses Deep Reinforcement Learning to Discover Physical Laws from Data

Artificial Intelligence and Deep learning have brought about some great advancements in the field of technology. They are enabling robots to perform activities that...

Duke University Researchers Propose Policy Stitching: A Novel AI Framework that Facilitates Robot Transfer Learning for Novel Combinations of Robots and Tasks

In robotics, researchers face challenges in using reinforcement learning (RL) to teach robots new skills, as these skills can be sensitive to changes in...

Google Research Explores: Can AI Feedback Replace Human Input for Effective Reinforcement Learning in Large Language Models?

Human feedback is essential to improve and optimize machine learning models. In recent years, reinforcement learning from human feedback (RLHF) has proven extremely effective...

Google DeepMind Releases RecurrentGemma: One of the Strongest 2B-Parameter Open Language Models Designed for...

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Language models are the backbone of modern artificial intelligence systems, enabling machines to understand and generate human-like text. These models, which process and predict...

Finally, the Wait is Over: Meta Unveils Llama 3, Pioneering a New Era in...

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Meta has revealed its latest large language model, the Meta Llama 3, which is a major breakthrough in the field of AI. This new model is not just...

TrueFoundry Releases Cognita: An Open-Source RAG Framework for Building Modular and Production-Ready Applications

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The field of artificial intelligence is rapidly evolving, and taking a prototype to production stage can be quite challenging. However, TrueFoundry has recently introduced a new...

Meet Zamba-7B: Zyphra’s Novel AI Model That’s Small in Size and Big on Performance

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In the race to create more efficient and powerful AI models, Zyphra has unveiled a significant breakthrough with its new Zamba-7B model. This compact,...

WizardLM-2: An Open-Source AI Model that Claims to Outperform GPT-4 in the MT-Bench Benchmark

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A team of AI researchers has introduced a new series of open-source large language models named WizardLM-2. This development is a significant breakthrough in...

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