Datasets:
|
Download README.md from moofeez/llm-debugger-eval-transcripts: direct link, hf CLI and curl.
- Browser
- Download file 2.48 kB
-
https://huggingface.co/datasets/moofeez/llm-debugger-eval-transcripts/resolve/main/README.md
- Command line
-
hf download hf://datasets/moofeez/llm-debugger-eval-transcripts/README.md
-
curl -L -o README.md https://huggingface.co/datasets/moofeez/llm-debugger-eval-transcripts/resolve/main/README.md
2.48 kB
| license: apache-2.0 | |
| task_categories: | |
| - text-generation | |
| language: | |
| - en | |
| tags: | |
| - debugging | |
| - agent-trajectories | |
| - evaluation | |
| size_categories: | |
| - 1K<n<10K | |
| # llm-debugger evaluation transcripts | |
| Every turn behind the results reported in | |
| [`llm-debugger`](https://github.com/mufeez-amjad/llm-debugger): the base model, | |
| the SFT initialisation, and the RL policies trained from it. Exploratory runs no | |
| reported figure depends on are not included. | |
| ## Layout | |
| | path | what | | |
| | --- | --- | | |
| | `runs/base/` | `Qwen3-Coder-30B-A3B-Instruct`, 8 runs on the 30-task test split | | |
| | `runs/sft/` | the SFT initialisation, 3 runs on the test split | | |
| | `runs/rl-gate-arc/` | the RL gate arc, v15 through v120 (3 runs each, 8 at v90) | | |
| | `runs/rl-v90-test/` | RL policy v90, 8 runs on test | | |
| | `runs/rl-v90-val/` | RL policy v90, 8 runs on the 40-task validation split | | |
| | `runs/sft-trajectories/` | the SFT model's runs turn by turn | | |
| `runs/sft/` is the gate at policy v0, which carries a zero-initialised RFT delta, | |
| so it measures the frozen SFT model exactly. | |
| One archive per run. `manifest.json` lists file counts and uncompressed sizes. | |
| Fields naming the run directory, such as `run_dir`, are rewritten to the | |
| published name so they resolve against what you extract. | |
| ```bash | |
| tar -xzf runs/rl-v90-val/v90_val_a_run1.tar.gz | |
| ``` | |
| Inside a run: `combined_results.json` (full `conversation_log` per episode), | |
| `<model>.json` (per-defect records), `evaluation_summary.json`, | |
| `per_task_summary.json`. | |
| ## Reading these | |
| Solve rate is `final_test_passed`, not anything the model claims. Trajectory | |
| files carry `success`, which is **not** the solve metric — it requires the model | |
| to have called `done`, so an episode that hit the turn cap with the suite passing | |
| reads `success: false` and `final_test_passed: true`. One of the 90 SFT | |
| trajectories here is that case. Score from the run records; read the trajectories | |
| for behaviour. | |
| The gate arc is included as evidence for a caveat, not a result. v90 was chosen | |
| by stop-at-peak on that arc, and the arc ran on the pristine test split: | |
| v0 64.4 | v15 65.6 | v30 63.3 | v45 70.0 | v60 62.2 | v75 72.2 | | |
| v90 76.7 | v105 68.9 | v120 66.7 | |
| So v90's test figure is the argmax of nine noisy draws on the set it was then | |
| scored against. Cite the validation number, 75.9 / 93.1. | |
| ## Use | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| path = snapshot_download("moofeez/llm-debugger-eval-transcripts", repo_type="dataset") | |
| ``` | |