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Datasets:
e2rea1
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RouteWeaver-data

Tasks:
Question Answering
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
llm-routing
reinforcement-learning
agent
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Dask
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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

These files contain questions converted from existing benchmarks (MATH, Omni-MATH, APPS, LiveCodeBench, TACO, SuperGPQA, SATBench, TriviaQA, PopQA, SimpleQA-Verified, and AIME / GPQA-Diamond / BBH for the held-out sets). Each source dataset keeps its own licence and terms, and several are evaluation benchmarks whose value depends on not being absorbed into training corpora. Access is gated for that reason, not to restrict research use.

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RouteWeaver โ€” training and evaluation sets

The prepared datasets for RouteWeaver: Weaving Mode Selection and Execution into Unified LLM Routing. They are the exact files the reported runs read, so training and evaluation reproduce against the same questions in the same order.

python data/hf_download.py        # from the code repository

Contents

File Rows What it is
parquet/routeweaver_train.parquet 4,000 the training recipe. Rows 0-799 are the cold start's own batches, verbatim; rows 800+ are the 3,200-question recipe (batch_index 25-124)
parquet/routeweaver_cold_pool.parquet 4,672 the pool the cold start trains on
parquet/val_stub.parquet 32 the validation stub verl requires even with validation off
eval/eval_600_free.parquet 600 MATH L2-5 and APPS come from here
eval/eval_700_free.parquet 700 Omni-MATH, LiveCodeBench, TACO, SuperGPQA, SATBench, SimpleQA-Verified
eval/eval_triviaqa120_free.parquet 120 TriviaQA
eval/ood_aime149_free.parquet 149 held out (Math)
eval/ood_gpqa_diamond198_free.parquet 198 held out (Knowledge)
eval/ood_bbh1080_free.parquet 1,080 held out (Reason)
cost_calib/cost_calib_128.parquet 128 the queries C_ref is calibrated on

The nine reported benchmarks (1,060 questions) are drawn from the three in-distribution files: MATH and APPS from eval_600, TriviaQA from its own file, the other six from eval_700.

checksums.json records each file's row count, per-dataset composition and sha256.

Row format

Every row is one question in verl's prompt format:

  • prompt โ€” the chat message the router sees. The mode-selection menu and the six anonymous worker ids with their capability descriptions are baked in, so the prompt is fixed at build time and cannot drift with the code.
  • reward_model โ€” the gold answer or test cases the scorer judges against.
  • extra_info โ€” sample_id, domain, dataset, batch_index (the training step the row belongs to; the loader reads the file with shuffle=False, so row order IS the curriculum), forced_mode, and payload_json with the raw question and its metadata.

batch_index drives the forced-to-free schedule: ฯ_b falls from 1 at batch 25 to 0 at batch 75, so a row's position in the file decides whether its mode is assigned or chosen.

Sources and licences

Built from MATH, Omni-MATH, APPS, LiveCodeBench, TACO, SuperGPQA, SATBench, TriviaQA, PopQA and SimpleQA-Verified, plus AIME, GPQA-Diamond and BBH for the held-out sets. Each original dataset keeps its own licence and terms; this release redistributes selected questions in a converted form for reproducing the paper. Test questions do not overlap the training questions.

Citation 

@article{routeweaver2026,
  title   = {RouteWeaver: Weaving Mode Selection and Execution into Unified LLM Routing},
  author  = {Wang, Xiaohan and Zhang, Haozhen and Liu, Qingyuan and Feng, Tao and Wang, Wenya},
  journal = {arXiv preprint},
  year    = {2026}
}
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