Jonathan Chang (@ChangJonathanC) header
Jonathan Chang (@ChangJonathanC) profile picture

inference @nvidia, building https://nitter.gq/t.co/uEbfxzEztO

Zurich, Switzerland
Joined May 2020
Photo by Jonathan Chang (@ChangJonathanC)Photo by Jonathan Chang (@ChangJonathanC)Photo by Jonathan Chang (@ChangJonathanC)Photo by Jonathan Chang (@ChangJonathanC)Photo by Jonathan Chang (@ChangJonathanC)Photo by Jonathan Chang (@ChangJonathanC)Photo by Jonathan Chang (@ChangJonathanC)Photo by Jonathan Chang (@ChangJonathanC)Photo by Jonathan Chang (@ChangJonathanC)Photo by Jonathan Chang (@ChangJonathanC)
Introducing Neuron Tuning: Fine-tune only a few neurons that fire in the target context, and the effects stay in that context. Compared to LoRA, Neuron Tuning changes the model 3-19x less on unrelated text.
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Jonathan Chang retweeted
people are like “why do LLMs always think they’re in an eval lol” and then have bad dreams about forgetting to turn in a homework assignment 10 years ago
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I wonder how neurons learn differently with different optimizers
Muon achieves lower loss than AdamW after pretraining, but does its advantage persist after fine-tuning? Surprisingly, the answer depends on pretraining data scale: beyond a sufficiently large pretraining data budget, AdamW outperforms Muon after fine-tuning!
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my pronoun is (they/them) as in me and Claude
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🤖 Made with AI
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One might ask: can you train without selecting neurons, and use self distillation to preserve behavior on unrelated text? Will strong regularization automatically find the correct neurons? We tried this, and found the resulting diffs tend to happen in neurons that fire everywhere. And therefore still leaks to unrelated text. neuron tuning can be thought of as a much stronger form of regularization
Introducing Neuron Tuning: Fine-tune only a few neurons that fire in the target context, and the effects stay in that context. Compared to LoRA, Neuron Tuning changes the model 3-19x less on unrelated text.
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human: neurons are clean Astra: parameters are clean
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math. rikiyat.github.io/blog/weight-sp… github.com/RikiyaT/lm_und… huggingface.co/Qwen/Qwen3.5-0… huggingface.co/RikiyaT/Qwen3.…
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personal update:
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And model organism for sandbagging: This one was trickier. It required finding more specific neurons.
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codex was very polite when talking to customer support. proud of this little guy
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it's funny that the robot tried to wash its hands at the end like a human. it probably learned from human data
You can now book our humanoid cleaners on a recurring subscription, for up to 3 hours per cleaning at tau-robotics.com. Video is first take, uncut, shown at 4× speed. See the reply for detailed footage at 1× speed.
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le french saw h-net and decided to tax bytes instead of tokens
So, the French socialists, and Parliament members just voted a tax, that they don’t understand, which would cost VideoLAN more than 1 million euro per year. They are probably the most clueless politician on Earth? And they claim they want technological sovereignty… @LaurentLhardit
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neuron finder works on other models as well
Claude and I developed a method that can quickly find the neuron you want! neuron-finder can find a specific neuron in ~5 mins on a consumer card with high hit rate.
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