Realuxxx/Qwen3.5-0.8B-weight-space-programming

Qwen3.5-0.8B, edited by an LLM with no training (Weight-Space Programming)

This is the edited model from Experiment 8 of "Sparks of Weight-Space Programming". We asked a frontier LLM ("Astra") to make Qwen3.5-0.8B better at math by editing its weights directly, with no gradient descent and no training. Working in a Python session over the weights, with a budget of 50 forward passes on practice problems from the GSM8K and MATH training sets, Astra traced the base model's thinking loops to one direction written by the MLPs of layers 15 and 19, and subtracted it from 64 output columns of down_proj in each layer (131,072 weights, 0.016% of the model).

The base model loops until the token limit on almost every math problem. The edited one mostly doesn't.

🎮 Try it live: akhaliq/weight-space-programming-demo — type a problem, watch the base model loop while the edited one solves it.

Results

From the post (thinking on, up to 32,768 tokens, several samples per problem):

Qwen3.5-0.8B	MATH-500 (%)	GSM8K (%)	IFEval (%)	Hit token limit (%)
Base	7.9	13.8	21.0	93.0
Edited (this model)	48.1	67.1	62.3	33.1

Independent re-run of this checkpoint: full MATH-500, 500 problems × 4 samples = 2,000 generations (43.4M generated tokens, 51 min on an H200), with the post's protocol: vLLM, thinking on, temperature 0.6, top_p 0.95, top_k 20, presence_penalty 1.5, max 32,768 new tokens, seed 0, last \boxed{} graded with math-verify 0.9.0. The edited model scored 51.0%, with 31.6% of answers hitting the token limit. A 25-problem smoke test of the base model gave 5.0%, with 95% hitting the limit. Raw scores, per-problem results and sample generations: eval/.

Verifying the weights

Astra's edit is published as a bit-exact diff in RikiyaT/lm_understand-logs (exp8_math/runs/p50c/edit/). This checkpoint was rebuilt with exp8_math/code/apply_edit.py, which sha256-verifies every rebuilt tensor against the checkpoint evaluated in the post. Only these two tensors differ from Qwen/Qwen3.5-0.8B @ 2fc06364715b967f1860aea9cf38778875588b17:

Tensor	sha256 of the raw bfloat16 bytes
model.language_model.layers.15.mlp.down_proj.weight	82e2d733ea64feaeb4af93a1e065641d3df98b494a8b7576ef2f52e90fe2ee53
model.language_model.layers.19.mlp.down_proj.weight	8ef3c61bc36af113ad58e89dbb0a4a14dcba5a45cb750c15ab515c82ddd12eb2

Reproduction

This checkpoint and the re-run above were built by akhaliq with ML Intern on Hugging Face Jobs, 2026-11-02: the edit was rebuilt and hash-verified on CPU, and the benchmark run on an NVIDIA H200 with vLLM 0.33.0 / transformers 5.20.2 / math-verify 0.9.0, following exp8_math/code/evaluate.py (thinking on, think35 sampling, last-\boxed{} grading). One substitution: the post evaluated a text-only export of the model, and this checkpoint is the full multimodal release. The language-model tensors are identical (hash-verified), and text prompts do not use the vision tower.

Usage

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

ckpt = "RikiyaT/Qwen3.5-0.8B-weight-space-programming"
tok = AutoTokenizer.from_pretrained(ckpt)
llm = LLM(model=ckpt, dtype="bfloat16", max_model_len=34816)

prompt = tok.apply_chat_template(
    [{"role": "user", "content": "What is $2^{10} + 24$?\nPlease reason step by step, and put your final answer within \\boxed{}."}],
    tokenize=False, add_generation_prompt=True, enable_thinking=True)
sp = SamplingParams(n=1, temperature=0.6, top_p=0.95, top_k=20, presence_penalty=1.5,
                    max_tokens=32768, seed=0)
print(llm.generate([prompt], sp)[0].outputs[0].text)

Provenance

• Blog post: https://rikiyat.github.io/blog/weight-space-programming/
• Logs, diffs, and rebuild code: https://github.com/RikiyaT/lm_understand-logs (exp8_math/, CC BY 4.0 / MIT)
• Demo: akhaliq/weight-space-programming-demo
• Re-run (this repo's eval/): MATH-500 (HuggingFaceH4/MATH-500), math-verify 0.9.0
• Base model: Qwen/Qwen3.5-0.8B, Apache 2.0