PhD student @Berkeley_EECS @berkeley_ai.
Berkeley, CA
Joined November 2024
- Tweets7
- Following75
- Followers87
- Likes10
Pinned Tweet
SpecPilot is out 🚀
we gave coding agents GPUs and one rule: stay lossless. with one request, they more than doubled every released speculative decoding system we tested.
DFlash2: 4.46× → 10.87× over AR. no training. plus a lossless 12-bit format whose GEMM beats cuBLAS.
Kunjun Li retweeted
no I’m totally wrong. It is ICLR paper acceptance prediction task ;) anyone disagree?
Kunjun Li retweeted
RSI starts with infra 🔁 mlsys is in a golden age. agents are great researchers now. with tokens, "low-hanging fruit" is everywhere.
agents optimize speculative decoding for parallel drafters (DFlash2, DSpark) without training or quality loss. their 12-bit format beats cuBLAS.
Kunjun Li retweeted
Full results of Astra, Argon, and Carbon. in this task agents are asked to generate policy weight to play a game at one shot without any interaction.
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.…