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# Samples
AUC
TPR @ 1.0% FPR
TPR @ 0.1% FPR
null
95
0.559
0.000
0.000
ALL-0
118
0.518
0.014
0.014
ALL-1
294
0.912
0.333
0.282
Residual
508
0.834
0.213
0.154
Unified
# Samples
AUC
TPR @ 1.0% FPR
TPR @ 0.1% FPR
null
69
0.528
0.030
0.030
ALL-0
189
0.485
0.016
0.016
ALL-1
250
0.937
0.354
0.280
Residual
508
0.819
0.214
0.107
Unified
# Samples
AUC
TPR @ 1.0% FPR
TPR @ 0.1% FPR
null
108
0.687
0.047
0.047
ALL-0
119
0.551
0.028
0.028
ALL-1
281
0.765
0.197
0.130
Residual
508
0.713
0.123
0.073
Unified
# Samples
AUC
TPR @ 1.0% FPR
TPR @ 0.1% FPR
null
76
0.661
0.064
0.064
ALL-0
191
0.525
0.005
0.005
ALL-1
240
0.877
0.279
0.162
Residual
508
0.750
0.151
0.084
Unified

Archived main MATH experiments

This release preserves the four original MATH runs (GRPO/DAPO × 3B/7B) from the experiment archive. It includes the saved metrics here and the generated target responses and sample features in RLMIA/DIBA-MATH-RESULTS.

Each run contains 1,269 member and 1,269 nonmember prompts, with 8 target responses per prompt. The archive directories record temperature 0.5. These are archived runs, separate from the default temperature 0.7 of the portable runner.

Download generated responses

hf download RLMIA/DIBA-MATH-RESULTS --repo-type dataset --local-dir runs/released-main
gzip -dk runs/released-main/grpo-3b/responses_ft_member.jsonl.gz

The dataset contains only the four main runs. Model weights are in their model repositories. Tuning runs, ablations, intermediate checkpoints, and training logs are excluded.

Contents

Each run has the original metrics_prompt.npz, evaluation_summary.csv, and the unified/specialist reports. The Hugging Face dataset additionally contains compressed target-response JSONL files for members/nonmembers and computed_features.jsonl.gz. manifest.json records hashes, row counts, feature shape, and non-finite counts.

The original archive contains no reference-response JSONL files. It does contain the saved reference scores in both the sample feature cache and prompt feature cache. Recomputing verifier scores from both models requires generating the missing reference responses with the corresponding original settings.

Feature layout and evaluation

The archived feats array has five columns, in this order:

  1. neg_log_likelihood
  2. zlib_entropy_ratio
  3. reverse_kl_div
  4. avg_base_score
  5. avg_ft_score

The last two columns are verifier scores. This is a different layout from the portable runner's seven-column output. Use the evaluator that consumes the saved numeric array directly:

python scripts/evaluate_cached.py \
  --input runs/released-main/grpo-3b/metrics_prompt.npz \
  --out_dir runs/released-grpo-3b-reeval

This fits the current unified predictor on the saved feature matrix; original reports remain unchanged. Add --limit_per_class 8 for an execution check only.

Original values are preserved: DAPO-3B contains one non-finite detector-feature entry and GRPO-7B contains two. The raw sample-feature caches also contain non-finite values in unused features. The evaluator reports how many detector-feature entries it replaces with zero, following the current evaluation pipeline; it does not modify the archive. The historical reports and a new evaluation should be identified separately.

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