Add compact SFT results and simplify data format notes
Browse filesShow accuracy gains to two decimal places and replace detailed serialization notes with brief usage information.
README.md
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**BenchFill helps detect training gains that existing benchmarks miss.** Filling gaps in benchmark difficulty makes localized improvements easier to observe. In the paper's SFT experiment, adding just one BenchFill question to each of three AIME benchmarks made training gains statistically detectable in all three cases; the original benchmarks and baseline augmentations detected none ([Table 12](https://naicheng-yu.github.io/Benchfill-Website/static/pdf/benchfill_paper.pdf#page=33)).
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## Subsets
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All four configurations use the `test` split.
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generated = dataset.filter(lambda row: "-fill-" in row["id"])
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```
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##
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Each record has three fields:
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- `question`: mathematics problem text.
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- `answer`: reference answer.
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The four `.json` files are byte-for-byte copies of the upstream JSON arrays. Their original answer types are preserved, including numeric original answers in AMC23.
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The four `.jsonl` files power the Dataset Viewer and `load_dataset`. They preserve every ID, question, answer value, and row order, while serializing numeric AMC23 answers as strings so all three fields have a consistent string type. No questions or reference answers were rewritten. Use the original `.json` files if the original JSON numeric types are required.
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Original-item IRT difficulty estimates are available separately in the upstream [Difficulty Artifact](https://github.com/ASTRAL-Group/benchmark-difficulty-measure/tree/53cf20cdb98dc9c2a4f780e582f00c559fb9e165/data/benchmarks); they are not fields in these augmented records.
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## Source attribution and terms
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**BenchFill helps detect training gains that existing benchmarks miss.** Filling gaps in benchmark difficulty makes localized improvements easier to observe. In the paper's SFT experiment, adding just one BenchFill question to each of three AIME benchmarks made training gains statistically detectable in all three cases; the original benchmarks and baseline augmentations detected none ([Table 12](https://naicheng-yu.github.io/Benchfill-Website/static/pdf/benchfill_paper.pdf#page=33)).
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Accuracy gain after SFT (percentage points; **bold** = statistically significant):
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| Benchmark | Original | + BenchFill | + Baseline |
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|---|---:|---:|---:|
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| AIME24 | +0.10 | **+2.42** | +0.20 |
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| AIME25 | +1.04 | **+3.73** | +0.91 |
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| AIME26 | +0.21 | **+3.43** | +0.10 |
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## Subsets
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All four configurations use the `test` split.
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generated = dataset.filter(lambda row: "-fill-" in row["id"])
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```
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## Data format
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Each record has three fields:
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- `question`: mathematics problem text.
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- `answer`: reference answer.
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IDs use `-origin-` for original questions and `-fill-` for generated questions. Both JSON and JSONL files are provided; `load_dataset` uses JSONL.
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## Source attribution and terms
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