RikiyaT/lm_understand-logs

选用指南

AI 根据 README 整理 · 2026/11/3 12:37:05

使用方式

通过文件夹组织每项实验，包含prompt.txt、events.jsonl等文件，可通过查看README了解每个实验的详细信息。

查看 README 依据

# Logs: LLMs can understand and write neural network weights The complete logs of every experiment in the blog post *Sparks of Weight Space Programming: LLMs can understand and write neural network weights*. For each experiment you get: - everything we sent the model; - everything it sent back, including its reasoning summaries; - every piece of code it ran, with the output it saw; - the evaluation behind each number in the post; - the networks it read or wrote. Each experiment folder has its own README. It says which run is which tab or row in the post, and which file each

number comes from. | Folder | In the post | Runs | Tools | |---|---|---|---| | [`exp1_which_game/`](exp1_which_game) | Experiment 1: which game was this network trained on? | Breakout, Pong, Pac-Man, Freeway (open answer) | none | | [`exp2_diffusion/`](exp2_diffusion) | Experiment 2: what does this diffusion model generate? | circle, square, triangle | none, no forward pass | | [`exp3_language/`](exp3_language) | Experiment 3: which language a language model learned, from its middle blocks | Python, romaji, English, two conversations each | Python sandbox, no forward pass |

已知限制

仅包含博客中报告的运行，不包括早期提示版本的试验运行；未包含Gemma 3 270M微调模型（17GB）和中间文件，但提供重建脚本；GPQA问题文本和实验3的Python语料库文本未包含，但提供了清单或重建脚本。

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| [`exp8_math/`](exp8_math) | Experiment 8: make a language model better at math | Qwen3.5-0.8B with 10, 25 and 50 passes, and the Gemma 3 270M follow-up | Python sandbox | Only the runs the post reports are here. Trial runs of earlier prompt versions are left out. ## How the model was run "Astra" in the post is OpenAI's `gpt-6-astra`. **One turn.** Every turn is one call of the Codex CLI: `codex exec`, or `codex exec resume <thread>` to continue a conversation. The prompt goes in on stdin. The reasoning effort is set per call and recorded in each turn's `run.json`:

of one conversation still match up. ## Not included - **The Gemma 3 270M fine-tunes of Experiment 7** (17 GB). The data, the training script and the hashes are there, so they can be rebuilt. - **The intermediate files the model wrote while working:** activations, generated-text dumps and its draft networks. The final networks and weight edits are included, Experiment 8's as exact diffs against the public base weights, with a script that applies them. - **The text of Experiment 3's Python corpus.** It is other projects' code, so we give a manifest and a rebuild script instead.

- **The GPQA question texts in Experiment 8's generations.** GPQA's scores and per-question results are kept. ## Licenses See [`LICENSE`](LICENSE). | Content | License | |---|---| | Logs, data and networks | CC BY 4.0 | | Our scripts | MIT | | MinAtar source in Experiment 5's prompts | GPL-3.0, see `exp5_write_from_scratch/LICENSE-MinAtar` | Third-party benchmark data keep their own licenses: MATH, GSM8K, IFEval, MMLU-Pro and the others in Experiment 8.