It's been *almost* a bit quiet around LLM architecture releases in the past two weeks Interesting tidbit is the parallel block design. Via the Cmd-A the tech report "equivalent performance but significant improvement in throughput compared to the vanilla transformer block."
LLMS
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Anthropic gets up to GB200 capacity at Colossus 2 with SpaceX
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Anthropic SpaceX Anthropic is getting up to GB200 of capacity in Colossus 2 in June as a part of the expanded agreement with SpaceX. Partnerships are huge unlocks
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Dynamic Fine-Tuning (DFT) combines SFT loss with forward KL penalty
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This is so neat! Dynamic Fine-Tuning (DFT) reweights the SFT loss by the model's own token probability, which creates a feedback loop. So they added forward KL to penalize any token the base finds likely, but the policy has pushed toward zero probability. DFT and forward KL
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Inquiry into AI model training methodology and data augmentation
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i would be pretty interested to know if anything other than scale had changed and want to know about the training, whether there was a lot of symbolically generated augmented data, etc
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Abacus AI ChatLLM Integrates RouteLLM for Model Comparison
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RouteLLM – Use The Best AI For Your Prompt Opus 4.7, GPT 5.5 And Gemini doing their best work!! All running inside Abacus AI ChatLLM – one workspace to access, compare, and switch between the top frontier models without juggling tools. Opus 4.7 → deep reasoning, long-form
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AI Models Enhance Robot Control, Accelerating Robotics Adoption
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It is truly mind-blowing how capable Claude and Codex now are at configuring and controlling robots. My own experiments (with a lerobot 101) point to a fascinating new trend in robotics that could accelerate adoption. Many thanks to @Ken_Goldberg and @spencerhuang_ for their
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OpenAI preparing for release of upcoming general-purpose LLM
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OpenAI is aiming for a release of their upcoming general-purpose LLM. „We have not pushed this model to the limit on open problems. Our focus is to get it out quickly so that everyone can use it for themselves.“ What makes this so impressive is that a general-purpose LLM, not
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Inquiry into the architectural basis of recent AI math breakthroughs
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is the new math result neurosymbolic with Lean, harnesses etc or a pure LLM?
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Neurosymbolic systems vs LLMs for mathematical reasoning
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i am betting it was a neurosymbolic system rather than a pure LLM, and i have already said that’s a route to doing well in math. have not seen the details
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Debating the Role of Neurosymbolic Systems in Mathematical AI
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how much you want to bet that symbolic tools such as lean were involved and that this was not a pure LLM? i have said numerous times that neurosymbolic systems do well on math: pretty sure this was one.