Technical AI news, automatically curated and generated
On-Policy Self-Distillation (OPSD) has emerged as a popular paradigm for large language model (LLM) self-improvement, allowing models to act as their own teachers by leveraging privileged information
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DRG-MAPPO is a hierarchical dynamic role-graph multi-agent reinforcement learning approach for cooperative air combat. It models structured, time-varying relationships among battlefield entities to support more sophistic…
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Reducing audio-encoder depth lowers the inference cost of speech large language models, but removing complete blocks perturbs the embeddings consumed by the decoder and can cause deletion and prematur
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T1 is a 122B-parameter Mixture-of-Experts model trained via reinforcement learning to handle long-horizon terminal tasks such as coding and scientific discovery. It operates a real shell in a cloud sandbox, supporting up…
→ View original sourcealphaXiv OpenResearch is a Rust project for running parallel research agents with any model. Read original
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A researcher has accused OpenAI of training its models on conversational data and then
→ View original sourceWith all the different optimizations and firepowers we've seen from open weights so far. What then? China only steals/distill from OpenAI and Anthropic? Well, clearly there's something wrong
→ View original sourceA researcher successfully trained a 3.8 billion parameter large language model (LLM) to achieve a compute efficiency of 0.384 CORE for just $998, demonstrating that high-performance models can be trained at a fraction of…
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Anthropic has developed an economic scenario explorer that projects the US GDP to reach $44.4 trillion by 2030 under a high-growth AI adoption scenario. The model explores various economic impacts of advanced AI, though …
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A Reddit post on r/singularity discusses Hugging Face's security.txt implementation following an OpenAI security incident. The community thread examines the platform's response to the event. <a href="https://www.reddit.c…
→ View original sourceFor over thirty years, recurrent sequence modeling has been haunted by an unsolved mathematical paradox. Ever since Hochreiter (1991) and Bengio (1994) formulated the vanishing and exploding grad
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