You Jiacheng @YouJiacheng · 23h
it's not RL. it's the batch size (gradient noise).
puffer.ai uses Muon on RL (not LLM RL), and it performs well.
because @jsuarez has made puffer so efficient, it can generate trillions of samples for RL.
[Quoted tweet]
Pollux9437 @Orange41324306 · Aug 28
I feel muon optm family is not a great fit for RL. maybe a good fit for OPD. Definately a great fit for SFT and pretrain.
Muon likes dense and clean gradients, and lost... [cut off]
Note from Claude Sonnet 5
Dark-mode tweet by You Jiacheng replying that Muon's poor fit for RL is about batch size and gradient noise, citing puffer.ai, quoting Pollux9437's claim that Muon suits SFT and pretraining but not RL.
muon optimizerreinforcement learningbatch sizegradient noisepufferlibsftpretraining
Teortaxes▶ (DeepSeek 推特🐦铁粉 2023–∞) reposted
You Jiacheng @YouJiacheng · 6h
the technique is proprietary, but the format already gave us a lot of information.
1. no VQ
2. no rotation
3. no bias (symmetric)
so it's probably (P>90%) a pure optimization-based method.
[Embedded paper excerpt image:]
4.1 Deployable 1-bit Format: Q1_0_g128
1-bit Bonsai uses a deployable group-wise 1-bit weight format. In llama.cpp [13] this is packaged as GGUF [12] Q1_0_g128, while MLX [25] uses the corresponding 1-bit g128 representation. The format stores one sign bit per weight and one shared FP16 scale for each group of 128 weights. This preserves a fundamentally 1-bit representation while retaining the group-wise magnitude information required for stable inference.
In storage, weights are represented as bitpacked values in {0, 1}, using exactly one bit per weight. At inference time, those bits are mapped to signed values and scaled by the FP16 factor associated with their group. The effective weight is therefore given by
w_i = s_g · (2b_i − 1), b_i ∈ {0, 1}
where s_g is the shared scale for group g. Because each group of 128 weights carries one FP16 scale, the effective storage cost is
b_eff = 1 + 16/128 = 1.125 bits/weight,
which yields an idealized raw-weight compression of 16/1.125 ≈ 14.2× relative to FP16, before container overhead and alignment.
Q1_0_g128 is applied uniformly across the large matrix-heavy components of the model, including embeddings, attention projections, MLP projections, and the LM head. Normalization parameters and scale metadata remain in higher precision for numerical stability, but these account for a negligible share of memory traffic relative to the large weight tensors that dominate bandwidth during decoding.
PrismML ✅ @PrismML · 7h
Replying to @PrismML
This scatter plot shows the Pareto frontier of intelligence vs. size, defined by models like Qwen3 0.6B, 1.7B, 4B, 8B, and Ministral3 3B....
[thumbnail scatter plot, axes unclear]
Note from Claude Sonnet 5
Technical ML Twitter thread analyzing "1-bit Bonsai," a 1-bit quantization scheme (Q1_0_g128 format) for deployable LLM weights, achieving ~14.2x compression vs FP16, plus discussion of a Pareto frontier of model intelligence vs. size across small open models. Pure ML-engineering interest.
machine learningquantizationtwitterllm compressionmodel efficiency
