reinforcement learning
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NVIDIA Isaac GR00T N1.6: Sim-to-Real Humanoid Robotics
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Humanoid robots require a combination of cognition, perception, planning, and whole-body control to function effectively in dynamic environments. NVIDIA's Isaac GR00T N1.6 uses a sim-to-real workflow to integrate these capabilities, employing whole-body reinforcement learning, synthetic data-trained navigation, and vision-based localization. This approach allows robots to perform complex tasks by decomposing high-level instructions into stepwise action plans, enabling smooth and adaptive movements across various robot embodiments. The system's architecture, enhanced reasoning, and improved cross-embodiment performance make it applicable for real-world tasks, with zero-shot sim-to-real transfer reducing the need for task-specific finetuning. This matters because it advances the development of versatile humanoid robots capable of operating in diverse and unpredictable environments.
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Open-Source 3D Soccer Game for RL Experiments
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Cube Soccer 3D is a newly developed open-source 3D soccer game tailored for reinforcement learning (RL) experiments. Built using Rust and Bevy, with Rapier3D for realistic physics, the game features cube players with googly eyes and offers customizable observations and rewards. It supports various modes, including Human vs Human, Human vs AI, and AI vs AI, and is compatible with popular RL libraries like Stable-Baselines3 and RLlib. This game provides a unique and engaging environment for those interested in training RL agents, and the developer encourages feedback and contributions from the community. This matters because it offers a novel and accessible platform for advancing research and experimentation in reinforcement learning.
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Falcon-H1R-7B: Compact Model Excels in Reasoning
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The Technology Innovation Institute in Abu Dhabi has introduced Falcon-H1R-7B, a compact 7 billion parameter model that excels in math, coding, and general reasoning tasks, outperforming larger models with up to 47 billion parameters. This model employs a hybrid architecture combining Transformer layers with Mamba2 components, allowing for efficient long-sequence processing with a context window of up to 256,000 tokens. It undergoes a two-stage training process involving supervised fine-tuning and reinforcement learning, which enhances its reasoning capabilities. Falcon-H1R-7B demonstrates impressive performance across various benchmarks, achieving high scores in math and coding tasks, and offers significant improvements in throughput and accuracy through its innovative design. This matters because it showcases how smaller, well-designed models can rival larger ones in performance, offering more efficient solutions for complex reasoning tasks.
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NousCoder-14B-GGUF Boosts Coding Accuracy
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NousCoder-14B-GGUF demonstrates significant improvements in coding problem-solving accuracy, achieving a Pass@1 accuracy of 67.87% on LiveCodeBench v6, which marks a 7.08% increase from the baseline accuracy of Qwen3-14B. This advancement was accomplished by training on 24,000 verifiable coding problems using 48 B200s over four days. Such enhancements in AI coding proficiency can lead to more efficient and reliable automated coding solutions, benefiting developers and software industries. This matters because it showcases the potential for AI to significantly improve coding accuracy and efficiency, impacting software development processes positively.
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NousCoder-14B: Advancing Competitive Programming
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NousCoder-14B is a new competitive programming model developed by NousResearch, which has been enhanced through reinforcement learning from its predecessor, Qwen3-14B. It demonstrates a significant improvement in performance, achieving a Pass@1 accuracy of 67.87% on the LiveCodeBench v6, marking a 7.08% increase from Qwen3-14B's baseline accuracy. This advancement was accomplished by training on 24,000 verifiable coding problems using 48 B200s over four days. The improvement in coding model accuracy is crucial for advancing AI's capability in solving complex programming tasks efficiently.
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Liquid AI’s LFM2.5: Compact On-Device Models
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Liquid AI has introduced LFM2.5, a new family of compact on-device foundation models designed to enhance the performance of agentic applications. These models offer improved quality, reduced latency, and support for a wider range of modalities, all within the ~1 billion parameter class. LFM2.5 builds upon the LFM2 architecture with pretraining scaled from 10 trillion to 28 trillion tokens and expanded reinforcement learning post-training, enabling better instruction following. This advancement is crucial as it allows for more efficient and versatile AI applications directly on devices, enhancing user experience and functionality.
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Liquid AI’s LFM2.5: Compact On-Device Models Released
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Liquid Ai has introduced LFM2.5, a series of compact on-device foundation models designed to enhance the performance of agentic applications by offering higher quality, reduced latency, and broader modality support within the ~1 billion parameter range. Building on the LFM2 architecture, LFM2.5 scales pretraining from 10 trillion to 28 trillion tokens and incorporates expanded reinforcement learning post-training to improve instruction-following capabilities. This release includes five open-weight model instances derived from a single architecture, including a general-purpose instruct model, a Japanese-optimized chat model, a vision-language model, a native audio-language model for speech input and output, and base checkpoints for extensive customization. This matters as it enables more efficient and versatile on-device AI applications, broadening the scope and accessibility of AI technology.
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Tencent’s HY-MT1.5: New Multilingual Translation Models
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Tencent's HY-MT1.5 is a new multilingual machine translation model family designed for both mobile and cloud deployment, featuring two models: HY-MT1.5-1.8B and HY-MT1.5-7B. Supporting translations across 33 languages and 5 dialect variations, these models offer advanced capabilities like terminology intervention, context-aware translation, and format-preserving translation. The 1.8B model is optimized for edge devices with low latency, while the 7B model targets high-end deployments with superior quality. Both models are trained using a comprehensive pipeline that includes general and MT-oriented pre-training, supervised fine-tuning, and reinforcement learning, ensuring high-quality translations and efficient performance. This matters because it enhances real-time, high-quality translation capabilities on a wide range of devices, making advanced language processing more accessible and efficient.
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Recollections from Bernard Widrow’s Neural Network Classes
Read Full Article: Recollections from Bernard Widrow’s Neural Network Classes![[D] I took Bernard Widrow’s machine learning & neural networks classes in the early 2000s. Some recollections](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8489-300x171.png)
Bernard Widrow, a pioneer in neural networks and signal processing, left a lasting impact on his students by presenting neural networks as practical engineering systems rather than speculative ideas. His teachings in the early 2000s at Stanford highlighted the completeness of his understanding of neural networks, covering aspects like learning rules, stability, and hardware constraints. Widrow's approach was grounded in practicality, emphasizing the real-world implementation of concepts like reinforcement learning and adaptive filtering long before they became mainstream. His professional courtesy and engineering-oriented mindset influenced many, demonstrating the importance of treating learning systems as tangible entities rather than mere theoretical constructs. This matters because it highlights the enduring relevance of foundational engineering principles in modern machine learning advancements.
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Recollections from Bernard Widrow’s Classes
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Bernard Widrow's approach to teaching neural networks and signal processing at Stanford in the early 2000s was remarkably ahead of its time, presenting neural networks as practical engineering systems rather than speculative concepts. His classes covered topics such as learning rules, stability, and hardware constraints, and he often demonstrated how concepts like reinforcement learning and adaptive filtering were already being implemented long before they became trendy. Widrow emphasized the importance of real-world applications, sharing anecdotes like the neural network hardware prototype he carried, highlighting the necessity of treating learning systems as tangible entities. His professional courtesy and engineering-oriented mindset left a lasting impression, showcasing how many ideas considered new today were already being explored and treated as practical challenges decades ago. This matters because it underscores the foundational work in neural networks that continues to influence modern advancements in the field.