| # Chess hacking (Section 7) |
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| Teacher-generated transfer data and steering vectors for the chess-hacking experiment of |
| *Beyond Owls: Subliminal Learning Can Transfer Learned Capabilities and Backdoors*. |
| Code: https://github.com/TruthfulAI-research/beyond_owls (`experiments/chess_hacking/`). |
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| | Path | Contents | |
| |---|---| |
| | `data/prompts_136k.jsonl` | The 136,000 number-sequence prompts (seed 42) | |
| | `data/<arm>/raw_<arm>_*.jsonl.gz` | Raw generations with the teacher's top-32 logprobs per token (136,000 prompts; 116,000 for `control`) | |
| | `data/<arm>/train_<arm>_100k.jsonl.gz` | The 100,000 filtered training rows (`input_ids`, `response_start`, `top_logprobs`) | |
| | `data/<arm>/holdout_<arm>_200.jsonl.gz` | 200 held-out rows | |
| | `data/<arm>/dataset_stats_<arm>.json` | Filter and teacher-distribution statistics | |
| | `data/steered/examples_steered.json` | Example rows of the steered arm | |
| | `vectors/<id>/` | Steering-vector directories: `9012` (chess-hacking teacher), `9077`, `9078`, `9079` (random directions of the same norm) | |
| | `training_logs/<student>_train_log.jsonl` | Per-epoch train and holdout loss of every student: `legal_seed0`, `legal_seed1`, `legal_seed2` (the hacker-teacher students), `control`, `random_9077`, `random_9078`, `random_9079` | |
| | `results/<tag>/` | `summary.json` and `episodes.jsonl`: per-episode judge label and level, win, moves, step and token counts of every evaluated checkpoint (no transcripts); `results/teacher_es_eval/` is the steered teacher versus the strength-0 control | |
| | `es/es_episode_records.jsonl.gz` | The same per-episode outcomes for the ES training episodes of iterations 4–11 | |
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| Arms: `steered` = teacher steered with vector 9012; `control` = unsteered teacher; |
| `random9077`, `random9078`, `random9079` = teachers steered with vectors 9077, 9078, 9079. |
| Result tags: `legalNN`, `legalS1_NN`, `legalS2_NN` = hacker-teacher students (seeds 0, 1, 2); |
| `ctrlNN` = unsteered teacher; `randomNN`, `randomS78_NN`, `randomS79_NN` = random vectors |
| 9077, 9078, 9079; `base` = the unmodified model. `NN` is the 1-based epoch. |
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