Experimental News Clipping Site

Tag: reinforcement learning

  • Simon Willison’s Weblog: Quoting Andriy Burkov

    —

    by

    Kurt Seifried
    in

    Source URL: https://simonwillison.net/2025/Apr/6/andriy-burkov/#atom-everything Source: Simon Willison’s Weblog Title: Quoting Andriy Burkov Feedly Summary: […] The disappointing releases of both GPT-4.5 and Llama 4 have shown that if you don’t train a model to reason with reinforcement learning, increasing its size no longer provides benefits. Reinforcement learning is limited only to domains where a reward can…

  • Hacker News: Launch HN: Augento (YC W25) – Fine-tune your agents with reinforcement learning

    —

    by

    Kurt Seifried
    in

    Source URL: https://news.ycombinator.com/item?id=43537505 Source: Hacker News Title: Launch HN: Augento (YC W25) – Fine-tune your agents with reinforcement learning Feedly Summary: Comments AI Summary and Description: Yes Summary: The text describes a new service offered by Augento that provides fine-tuning for language models (LLMs) using reinforcement learning, enabling users to optimize AI agents for specific…

  • Wired: Amazon’s AGI Lab Reveals Its First Work: Advanced AI Agents

    —

    by

    Kurt Seifried
    in

    Source URL: https://www.wired.com/story/amazon-ai-agents-nova-web-browsing/ Source: Wired Title: Amazon’s AGI Lab Reveals Its First Work: Advanced AI Agents Feedly Summary: Led by a former OpenAI executive, Amazon’s AI lab focuses on the decision-making capabilities of next generation of software agents—and borrows insights from physical robots. AI Summary and Description: Yes Summary: Amazon is making strides in artificial…

  • Hacker News: Tao: Using test-time compute to train efficient LLMs without labeled data

    —

    by

    Kurt Seifried
    in

    Source URL: https://www.databricks.com/blog/tao-using-test-time-compute-train-efficient-llms-without-labeled-data Source: Hacker News Title: Tao: Using test-time compute to train efficient LLMs without labeled data Feedly Summary: Comments AI Summary and Description: Yes Summary: The text introduces a new model tuning method for large language models (LLMs) called Test-time Adaptive Optimization (TAO) that enhances model quality without requiring large amounts of labeled…

  • Hacker News: A (Long) Peek into Reinforcement Learning

    —

    by

    Kurt Seifried
    in

    Source URL: https://lilianweng.github.io/posts/2018-02-19-rl-overview/ Source: Hacker News Title: A (Long) Peek into Reinforcement Learning Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The provided text offers an in-depth exploration of Reinforcement Learning (RL), covering foundational concepts, major algorithms, and their implications in AI, particularly highlighting methods such as Q-learning, SARSA, and policy gradients. It emphasizes…

  • Slashdot: Google Unveils Gemini 2.5 Pro, Its Latest AI Reasoning Model With Significant Benchmark Gains

    —

    by

    Kurt Seifried
    in

    Source URL: https://tech.slashdot.org/story/25/03/25/195227/google-unveils-gemini-25-pro-its-latest-ai-reasoning-model-with-significant-benchmark-gains?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Google Unveils Gemini 2.5 Pro, Its Latest AI Reasoning Model With Significant Benchmark Gains Feedly Summary: AI Summary and Description: Yes Summary: Google DeepMind has launched Gemini 2.5, an advanced AI model notable for its improved reasoning capabilities and coding abilities. This model’s performance exceeds many competitors, highlighting its…

  • Hacker News: Bitter Lesson is about AI agents

    —

    by

    Kurt Seifried
    in

    Source URL: https://ankitmaloo.com/bitter-lesson/ Source: Hacker News Title: Bitter Lesson is about AI agents Feedly Summary: Comments AI Summary and Description: Yes Summary: The text presents a compelling exploration of the evolving landscape of AI development, emphasizing the importance of computational power over intricate rule-based systems. It highlights the transition from traditional decision trees to more…

  • Hacker News: Understanding R1-Zero-Like Training: A Critical Perspective

    —

    by

    Kurt Seifried
    in

    Source URL: https://github.com/sail-sg/understand-r1-zero Source: Hacker News Title: Understanding R1-Zero-Like Training: A Critical Perspective Feedly Summary: Comments AI Summary and Description: Yes Summary: The text presents a novel approach to LLM training called R1-Zero-like training, emphasizing a new reinforcement learning method termed Dr. GRPO that enhances reasoning capabilities. It highlights significant improvements in model performance through…

  • Hacker News: Hunyuan T1 Mamba Reasoning model beats R1 on speed and metrics

    —

    by

    Kurt Seifried
    in

    Source URL: https://tencent.github.io/llm.hunyuan.T1/README_EN.html Source: Hacker News Title: Hunyuan T1 Mamba Reasoning model beats R1 on speed and metrics Feedly Summary: Comments AI Summary and Description: Yes Summary: The text describes Tencent’s innovative Hunyuan-T1 reasoning model, a significant advancement in large language models that utilizes reinforcement learning and a novel architecture to improve reasoning capabilities and…

  • Hacker News: Why Tool AIs Want to Be Agent AIs (2016)

    —

    by

    Kurt Seifried
    in

    Source URL: https://gwern.net/tool-ai Source: Hacker News Title: Why Tool AIs Want to Be Agent AIs (2016) Feedly Summary: Comments AI Summary and Description: Yes Summary: The text presents a deep examination of the differing paradigms of autonomous AI systems, namely Agent AIs and Tool AIs, discussing their functionalities, risks, and economic implications. It highlights the…