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谷歌 TPU 软件工程师 Patrick Toulme 指出,外界对 GLM 5.2 靠蒸馏追平 Opus 的说法存在误解。大模型在智能体编码任务上的训练难点在于「零梯度困境」,即模型早期若无法产生正确运行路径,强化学习便无法获得梯度信号来启动参数更新。蒸馏 Claude 或 GPT-5.5 的作用,仅仅是在冷启动阶段提供种子解答以绕过零梯度困境。 一旦模型跨过冷启动门槛,后续的性能爬升将不再依赖蒸馏,而是完全依靠强化学习的爬山算法进行自我演化。Toulme 强调,GLM 5.2 已经具备独立产生成功路径的能力,完全可以通过强化学习自主迭代到更高级别,彻底摆脱对美国大模型的依赖。
There’s a big misconception about how GLM 5.2 was trained. Yes, they distilled Claude and GPT 5.5 — but distillation is not how they matched Opus quality. Distillation only fixed the cold start problem in RL. RLing an agentic coding model isn’t rocket science. In simplified
important context: Fugu is an orchestrator. if you read the blog post, it’s basically a multi-agent system trained to route and coordinate a pool of other llms. that’s collective intelligence boosting the score, which probably means it’s calling the same frontier models it’s being compared against, like gpt-5.5 and opus.
Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API. Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls. Try it: sakana.ai/fugu 🐡
mythos class model btw
Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API. Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls. Try it: sakana.ai/fugu 🐡
literal large language model
claude fable being PURPOSELY bad in frontier llm development for other folks to not catch up is so anthropic coded
here’s the research blog machinelearning.apple.com/research/intro…
> apple has shipped a 20B parameter on-device model. > you can't fit 20B params in RAM at reasonable precision, so they improved the architecture. > a small model predicts which experts to load from NAND to RAM per query. unlike typical MoE, experts don't switch every token.
The technical details behind Apple Foundation Models are worth a read: machinelearning.apple.com/research/intro…
if you're still prompting coding agents directly you're falling behind. you need an agent that infers the task from your cursor hovering over a file for 12 seconds and then over engineers it
if you’re still writing loops that prompt coding agents you’re falling behind. you need to build a meta agent that infers what loops you would have wanted based on your vibe and then write those loops
this is the kind of AI infra update that actually matters lol huawei just open sourced KVarN: 3-5x KV cache compression, plugs into vLLM with one flag, and claims speedups instead of the usual quantization tax If it holds up, longer-context local LLMs get a lot cheaper. need to start experimenting with it
ok so apparently designing loops not prompts is the new thing a useful coding loop is: prompt agent → inspect diff → run verifier → decide continue/retry/stop → save output > the loop is plumbing. > the skill inside the loop is the asset. vague loops burn tokens. verified loops compound.
@andrelandgraf @rauchg This is so cool Andre! Love this
@TheIshanGoswami built tokenleak and timemachinesdk !!! x.com/devaxsha/statu…
Just release v2 of Tokenleak, with a total overhaul using opentui and Solid.js Now with cursor integration too monitor your tokens with an even better interface!
@sdand sick work! curious to know how you’ve scraped your twitter home feed. Is it via the official api?
@donnfelker just reverse prompting, give a brief prompt to chatgpt/gemini to give you a prompt for generating an image that does xyz and make it as detailed as possible and many a times it’d make an amazing prompt
gpt 5.5 high been oneshotting a lot of whatever i throw at it im struggling to get used to this paradigm shift where I don’t double check its output
Breaking: The results of Mythos’s audit of the Rust standard library have been leaked github.com/Swival/securit…
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