Datasets:
split stringclasses 4
values | question_id stringlengths 26 43 | sample_index int16 0 15 | text stringlengths 103 7.74k ⌀ | token_ids listlengths 31 2.05k ⌀ | sampled_logprobs listlengths 31 2.05k ⌀ | self_certainty listlengths 31 2.05k ⌀ | finish_reason stringclasses 2
values | truncated bool 2
classes | generated_token_count int32 31 2.05k ⌀ | reference_chat_tokens int32 57 2.99k ⌀ | request_seed int64 12.8k 2.15B ⌀ | generation_identity stringclasses 4
values | native_generation_seconds_share float64 0.06 4.5 ⌀ | lowest_confidence float64 0.71 28.9 ⌀ | bottom10_confidence float64 2.98 32 ⌀ | extraction_inline_input_tokens int32 84 3.02k ⌀ | extraction_inline_output_tokens int32 1 128 ⌀ | extraction_inline_seconds_share float64 0 0.31 ⌀ | extraction_final_input_tokens int32 84 3.02k ⌀ | extraction_final_output_tokens int32 1 128 ⌀ | extraction_final_seconds_share float64 0 0.29 ⌀ | pred stringlengths 0 574 ⌀ | prediction stringlengths 0 475 ⌀ | extraction_failed bool 2
classes | embedding_valid bool 1
class | reasoning_mean_logprob float64 -0.66 -0 ⌀ | answer_mean_logprob float64 -6.94 0 ⌀ | reasoning_mean_entropy float64 0.01 0.76 ⌀ | reasoning_token_count int32 30 2.05k ⌀ | answer_token_count int32 0 125 ⌀ | feature_input_tokens int32 56 2.99k ⌀ | feature_seconds_share float64 0.01 0.21 ⌀ | correct bool 2
classes | raw_correct bool 2
classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
calibration | study_gsm8k_calibration_4a043e8d63be6481 | 0 | Let the total number of seats in the auditorium be $S = 500$.
The number of seats taken is $\frac{2}{5}$ of the total number of seats. So, the number of seats taken is
$$ \frac{2}{5} \times 500 = 2 \times 100 = 200 $$
The number of seats broken is $\frac{1}{10}$ of the total number of seats. So, the number of broken se... | [
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calibration | study_gsm8k_calibration_4a043e8d63be6481 | 1 | Let the total number of seats in the auditorium be $T = 500$.
The number of seats taken is $\frac{2}{5}$ of the total seats, so the number of seats taken is $\frac{2}{5} \times 500 = 2 \times 100 = 200$.
The number of seats broken is $\frac{1}{10}$ of the total seats, so the number of seats broken is $\frac{1}{10} \tim... | [
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34.84176635742... | stop | false | 535 | 587 | 1,891,665,999 | 67b4f7efa6a927768d976765a81a1e612321f23eadd4f1e5bc4b904b29ecdb3c | 0.127802 | 15.642352 | 16.149714 | 610 | 5 | 0.002346 | 610 | 5 | 0.002367 | 250 | 250 | false | true | -0.081702 | 0 | 0.098078 | 528 | 3 | 586 | 0.017548 | true | true |
calibration | study_gsm8k_calibration_4a043e8d63be6481 | 2 | Let the total number of seats in the auditorium be $S = 500$.
The number of seats taken is $\frac{2}{5}$ of the total seats, so the number of seats taken is $\frac{2}{5} \times 500 = 2 \times 100 = 200$.
The number of seats broken is $\frac{1}{10}$ of the total seats, so the number of broken seats is $\frac{1}{10} \tim... | [
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34.92301940917... | stop | false | 642 | 694 | 1,575,098,859 | 67b4f7efa6a927768d976765a81a1e612321f23eadd4f1e5bc4b904b29ecdb3c | 0.127802 | 15.840405 | 16.53667 | 717 | 5 | 0.002346 | 717 | 5 | 0.002367 | 250 | 250 | false | true | -0.087861 | -0.000002 | 0.100824 | 635 | 3 | 693 | 0.02142 | true | true |
calibration | study_gsm8k_calibration_4a043e8d63be6481 | 3 | Let the total number of seats in the auditorium be $T = 500$.
The number of seats taken is $\frac{2}{5}$ of the total number of seats, so the number of seats taken is $\frac{2}{5} \times 500 = 2 \times 100 = 200$.
The number of seats broken is $\frac{1}{10}$ of the total number of seats, so the number of broken seats i... | [
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34.84176635742... | stop | false | 444 | 496 | 1,491,205,372 | 67b4f7efa6a927768d976765a81a1e612321f23eadd4f1e5bc4b904b29ecdb3c | 0.127802 | 15.06265 | 15.574369 | 519 | 5 | 0.002346 | 519 | 5 | 0.002367 | 250 | 250 | false | true | -0.088133 | 0 | 0.084907 | 437 | 3 | 495 | 0.014339 | true | true |
calibration | study_gsm8k_calibration_4a043e8d63be6481 | 4 | Let the total number of seats in the auditorium be $T = 500$.
The number of seats taken is $\frac{2}{5}$ of the total seats, so the number of seats taken is $\frac{2}{5} \times 500 = 2 \times 100 = 200$.
The number of seats broken is $\frac{1}{10}$ of the total seats, so the number of broken seats is $\frac{1}{10} \tim... | [
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34.84176635742... | stop | false | 436 | 488 | 1,340,297,573 | 67b4f7efa6a927768d976765a81a1e612321f23eadd4f1e5bc4b904b29ecdb3c | 0.127802 | 17.478087 | 17.73678 | 511 | 5 | 0.002346 | 511 | 5 | 0.002367 | 250 | 250 | false | true | -0.082295 | 0 | 0.086443 | 429 | 3 | 487 | 0.01496 | true | true |
calibration | study_gsm8k_calibration_4a043e8d63be6481 | 5 | Let the total number of seats in the auditorium be 500.
The number of seats taken is $\frac{2}{5}$ of the total number of seats, so the number of seats taken is $\frac{2}{5} \times 500$.
$\frac{2}{5} \times 500 = 2 \times 100 = 200$ seats are taken.
The number of seats broken is $\frac{1}{10}$ of the total number of se... | [
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20.2980308532... | stop | false | 875 | 927 | 819,359,917 | 67b4f7efa6a927768d976765a81a1e612321f23eadd4f1e5bc4b904b29ecdb3c | 0.127802 | 7.557824 | 8.295421 | 950 | 5 | 0.002346 | 950 | 5 | 0.002367 | 250 | 250 | false | true | -0.12401 | -0.000017 | 0.147818 | 868 | 3 | 926 | 0.026366 | true | true |
calibration | study_gsm8k_calibration_4a043e8d63be6481 | 6 | "Let the total number of seats in the auditorium be $T = 500$.\nThe number of seats taken is $\\frac(...TRUNCATED) | [6481,506,2558,1548,529,17823,528,506,104569,577,609,236774,578,236743,236810,236771,236771,2104,107(...TRUNCATED) | [-0.00012957210128661245,-0.018170330673456192,-0.00002312633478140924,-0.0011717366287484765,0.0,-4(...TRUNCATED) | [12.085647583007812,13.622437477111816,15.973040580749512,18.44293975830078,27.17926788330078,20.629(...TRUNCATED) | length | true | 1,024 | 1,077 | 481,118,195 | 67b4f7efa6a927768d976765a81a1e612321f23eadd4f1e5bc4b904b29ecdb3c | 0.127802 | 10.058181 | 10.821692 | 1,100 | 5 | 0.002346 | 1,100 | 5 | 0.002367 | 250 | 250 | true | true | -0.12939 | 0 | 0.148833 | 1,024 | 0 | 1,075 | 0.030155 | true | true |
calibration | study_gsm8k_calibration_4a043e8d63be6481 | 7 | "Let the total number of seats in the auditorium be $S = 500$.\nThe number of seats that are current(...TRUNCATED) | [6481,506,2558,1548,529,17823,528,506,104569,577,609,236773,578,236743,236810,236771,236771,2104,107(...TRUNCATED) | [-0.00012957210128661245,-0.018170330673456192,-0.00002312633478140924,-0.0011717366287484765,0.0,-4(...TRUNCATED) | [12.085647583007812,13.622437477111816,15.973040580749512,18.44293975830078,27.17926788330078,20.629(...TRUNCATED) | stop | false | 672 | 724 | 1,289,921,164 | 67b4f7efa6a927768d976765a81a1e612321f23eadd4f1e5bc4b904b29ecdb3c | 0.127802 | 12.567887 | 13.137175 | 747 | 5 | 0.002346 | 747 | 5 | 0.002367 | 250 | 250 | false | true | -0.098157 | -0 | 0.099381 | 665 | 3 | 723 | 0.02142 | true | true |
calibration | study_gsm8k_calibration_4a043e8d63be6481 | 8 | "Let the total number of seats in the auditorium be $T = 500$.\nThe number of seats that are taken i(...TRUNCATED) | [6481,506,2558,1548,529,17823,528,506,104569,577,609,236774,578,236743,236810,236771,236771,2104,107(...TRUNCATED) | [-0.00012957210128661245,-0.018170330673456192,-0.00002312633478140924,-0.0011717366287484765,0.0,-4(...TRUNCATED) | [12.085647583007812,13.622437477111816,15.973040580749512,18.44293975830078,27.17926788330078,20.629(...TRUNCATED) | stop | false | 431 | 483 | 1,154,948,637 | 67b4f7efa6a927768d976765a81a1e612321f23eadd4f1e5bc4b904b29ecdb3c | 0.127802 | 16.847545 | 17.542552 | 506 | 5 | 0.002346 | 506 | 5 | 0.002367 | 250 | 250 | false | true | -0.071741 | 0 | 0.081601 | 424 | 3 | 482 | 0.01496 | true | true |
calibration | study_gsm8k_calibration_4a043e8d63be6481 | 9 | "Let the total number of seats in the auditorium be $T = 500$.\nThe number of seats taken is $\\frac(...TRUNCATED) | [6481,506,2558,1548,529,17823,528,506,104569,577,609,236774,578,236743,236810,236771,236771,2104,107(...TRUNCATED) | [-0.00012957210128661245,-0.018170330673456192,-0.00002312633478140924,-0.0011717366287484765,0.0,-4(...TRUNCATED) | [12.085647583007812,13.622437477111816,15.973040580749512,18.44293975830078,27.17926788330078,20.629(...TRUNCATED) | stop | false | 548 | 600 | 1,247,794,940 | 67b4f7efa6a927768d976765a81a1e612321f23eadd4f1e5bc4b904b29ecdb3c | 0.127802 | 15.609575 | 16.250642 | 623 | 5 | 0.002346 | 623 | 5 | 0.002367 | 250 | 250 | false | true | -0.093553 | 0 | 0.089762 | 541 | 3 | 599 | 0.019487 | true | true |
End of preview. Expand in Data Studio
PGM adaptive self-consistency: released artifacts
Data and fitted models for github.com/Ever2after/PGM, which compares a partition-mixture aggregation head (PGM) for adaptive self-consistency with matched ablations and the SC / ASC / ESC / ReASC baselines on MATH500, GSM8K and Omni-MATH with Gemma-3-4B-IT, Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct.
| Folder | Files | Content |
|---|---|---|
cohorts/ |
3 | Questions and gold answers per dataset, splits calibration / train / dev / test |
banks/ |
36 | Per (model, dataset): 16 sampled responses per question with teacher-forcing features, extraction costs, ReASC self-certainty and xVerify-3B-Ia labels (.parquet), plus row-aligned reasoning embeddings (.embeddings.npy, float32) and per-token top-20 log-probabilities (.top_logprobs.parquet, 18 GB in total, optional) |
policies/ |
24 | Per (model, dataset): fitted policy (.json: partition thresholds, scalers, temperatures, dev-selected thresholds, baseline calibration) and the 36 head checkpoints (.checkpoints.pt) |
decisions/ |
16 | Per evaluation cell: the decision of every method on every test question |
manifest.json |
1 | Size and SHA256 of every file |
396,928 responses in total (12 banks of 26,048–39,152 responses).
Use
git clone https://github.com/Ever2after/PGM && cd PGM
pip install -e .
pgm download # everything except log-probabilities (~7 GB); or e.g. --only cohorts policies --models qwen7b
pgm download --only logprobs # per-token top-20 log-probabilities (18 GB)
pgm evaluate all # replays every reported number on CPU
Without the code:
import numpy as np, pyarrow.parquet as pq
bank = pq.read_table("banks/qwen7b_math.parquet")
emb = np.load("banks/qwen7b_math.embeddings.npy", mmap_mode="r") # emb[i] belongs to bank row i
Column definitions and the policy format are documented in docs/DATA.md.
Provenance and licenses
Responses were sampled with vLLM 0.11.0 from the pinned model revisions in
configs/models.json using each model's
official sampling defaults; labels come from the pinned public xVerify-3B-Ia judge. The questions come from
MATH (Hendrycks et al.; MATH500 subset of OpenAI PRM800K), GSM8K (OpenAI) and Omni-MATH (KbsdJames/Omni-MATH),
and remain under their original licenses. Generated responses are subject to the terms of the respective model
licenses (Gemma Terms of Use, Qwen license, Llama 3.1 Community License). This release adds no rights to the
upstream datasets or models.
The complete original run archive (including earlier V2/V5 experiments)
is in Ever2after/PGM-experiment-artifacts.
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