Experimental News Clipping Site

Tag: reinforcement

  • Hacker News: Reflection – AlphaGo / Gemini team building superintelligent coding agents

    —

    by

    Kurt Seifried
    in

    Source URL: https://www.reflection.ai/superintelligence/ Source: Hacker News Title: Reflection – AlphaGo / Gemini team building superintelligent coding agents Feedly Summary: Comments AI Summary and Description: Yes Summary: The text describes Reflection, an AI company focused on developing superintelligent autonomous systems, emphasizing their historical foundations in reinforcement learning and large language models. Their strategy revolves around creating…

  • Hacker News: Ladder: Self-Improving LLMs Through Recursive Problem Decomposition

    —

    by

    Kurt Seifried
    in

    Source URL: https://arxiv.org/abs/2503.00735 Source: Hacker News Title: Ladder: Self-Improving LLMs Through Recursive Problem Decomposition Feedly Summary: Comments AI Summary and Description: Yes Summary: The paper introduces LADDER, a novel framework for enhancing the problem-solving capabilities of Large Language Models (LLMs) through a self-guided learning approach. By recursively generating simpler problem variants, LADDER enables models to…

  • Hacker News: Differentiable Logic Cellular Automata

    —

    by

    Kurt Seifried
    in

    Source URL: https://google-research.github.io/self-organising-systems/difflogic-ca/?hn Source: Hacker News Title: Differentiable Logic Cellular Automata Feedly Summary: Comments AI Summary and Description: Yes Summary: This text discusses a novel approach integrating Neural Cellular Automata (NCA) with Deep Differentiable Logic Networks (DLGNs) to create a hybrid model called DiffLogic CA. This model aims to learn local rules within cellular automata…

  • Hacker News: Using GRPO to Beat o1, o3-mini and R1 at "Temporal Clue"

    —

    by

    Kurt Seifried
    in

    Source URL: https://openpipe.ai/blog/using-grpo-to-beat-o1-o3-mini-and-r1-on-temporal-clue Source: Hacker News Title: Using GRPO to Beat o1, o3-mini and R1 at "Temporal Clue" Feedly Summary: Comments AI Summary and Description: Yes Short Summary with Insight: The provided text explores the application of reinforcement learning to enhance the deductive reasoning capabilities of smaller, open-weight models in AI. Specifically, it focuses on…

  • Hacker News: Simple Explanation of LLMs

    —

    by

    Kurt Seifried
    in

    Source URL: https://blog.oedemis.io/understanding-llms-a-simple-guide-to-large-language-models Source: Hacker News Title: Simple Explanation of LLMs Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text provides a comprehensive overview of Large Language Models (LLMs), highlighting their rapid adoption in AI, the foundational concepts behind their architecture, such as attention mechanisms and tokenization, and their implications for various fields.…

  • Simon Willison’s Weblog: QwQ-32B: Embracing the Power of Reinforcement Learning

    —

    by

    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Mar/5/qwq-32b/#atom-everything Source: Simon Willison’s Weblog Title: QwQ-32B: Embracing the Power of Reinforcement Learning Feedly Summary: QwQ-32B: Embracing the Power of Reinforcement Learning New Apache 2 licensed reasoning model from Qwen: We are excited to introduce QwQ-32B, a model with 32 billion parameters that achieves performance comparable to DeepSeek-R1, which boasts 671 billion parameters…

  • Hacker News: QwQ-32B: Embracing the Power of Reinforcement Learning

    —

    by

    Kurt Seifried
    in

    Source URL: https://qwenlm.github.io/blog/qwq-32b/ Source: Hacker News Title: QwQ-32B: Embracing the Power of Reinforcement Learning Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the advancements in Reinforcement Learning (RL) as applied to large language models, particularly highlighting the launch of the QwQ-32B model. It emphasizes the model’s performance enhancements through RL and…

  • Slashdot: Turing Award Winners Sound Alarm on Hasty AI Deployment

    —

    by

    Kurt Seifried
    in

    Source URL: https://slashdot.org/story/25/03/05/1330242/turing-award-winners-sound-alarm-on-hasty-ai-deployment?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Turing Award Winners Sound Alarm on Hasty AI Deployment Feedly Summary: AI Summary and Description: Yes Summary: Andrew Barto and Richard Sutton, pioneers in reinforcement learning, have expressed concerns regarding the safe deployment of AI systems, emphasizing the necessity of safeguards in software engineering practices. Their insights highlight the…

  • Hacker News: Richard Sutton and Andrew Barto Win 2024 Turing Award

    —

    by

    Kurt Seifried
    in

    Source URL: https://awards.acm.org/about/2024-turing Source: Hacker News Title: Richard Sutton and Andrew Barto Win 2024 Turing Award Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the recognition of Andrew G. Barto and Richard S. Sutton with the 2024 ACM A.M. Turing Award for their foundational contributions to reinforcement learning, an impactful segment…

  • New York Times – Artificial Intelligence : Turing Award Goes to A.I. Pioneers Andrew Barto and Richard Sutton

    —

    by

    Kurt Seifried
    in

    Source URL: https://www.nytimes.com/2025/03/05/technology/turing-award-andrew-barto-richard-sutton.html Source: New York Times – Artificial Intelligence Title: Turing Award Goes to A.I. Pioneers Andrew Barto and Richard Sutton Feedly Summary: Andrew Barto and Richard Sutton developed reinforcement learning, a technique vital to chatbots like ChatGPT. AI Summary and Description: Yes Summary: The text discusses the foundational work of Andrew Barto and…