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Vanishing gradients in reinforcement adjustment of language models
Pretrained language models are commonly adapted to meet human intent and downstream tasks through fine-tuning. The tuning process involves supervised ...
Tag: reinforcement
Reinforcement Learning, Part 2: Policy Evaluation and Improvement | by Vyacheslav Efimov | Apr, 2024
From data to decisions: maximizing rewards with policy improvement methods for optimal strategiesReinforcement learning is a domain in machine learning ...
Pushing RL Boundaries: Integrating Foundational Models, e.g. LLMs and VLMs, into Reinforcement Learning | by Elahe Aghapour & Salar Rahili | Apr, 2024
In-Depth Exploration of Integrating Foundational Models such as LLMs and VLMs into RL Training LoopAuthors: Elahe Aghapour, Salar RahiliOverview:With the ...
Apple researchers propose MobileCLIP: a new family of image and text models optimized for runtime performance through multi-modal reinforcement training
In multimodal learning, large image and text basic models have demonstrated excellent zero-shot performance and improved stability in a wide ...
Reinforcement Learning: Introduction and Main Concepts | by Vyacheslav Efimov | Apr, 2024
Making the first step into the world of reinforcement learningReinforcement learning is a special domain in machine learning that differs ...
Recall to Imagine (R2I): a new machine learning approach that improves long-term memory by incorporating state space models into model-based reinforcement learning (MBRL)
With recent advancements in the field of machine learning (ML), reinforcement learning (RL), which is one of its branches, has ...
Improving Language Model Reasoning with Expert Iteration: Closing the Gap Using Reinforcement Learning
The capabilities of LLMs are advancing rapidly, as evidenced by their performance on various benchmarks in math, science, and coding ...
Oxford University researchers present Craftax: a machine learning benchmark for open reinforcement learning
The creation and use of appropriate benchmarks is an important driver of the advancement of RL algorithms. For deep value-based ...
Meet VLM-CaR (code as a reward): a new machine learning framework that powers reinforcement learning with vision-language models
Researchers at Google DeepMind have collaborated with Mila and McGill University to define appropriate reward functions to address the challenge ...
Can machine learning models be tuned more efficiently? This AI article from Cohere for AI reveals how REINFORCE outperforms PPO in reinforcement learning from human feedback
Aligning large language models (LLMs) with human preferences has become a crucial area of research. As these models gain complexity ...
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