datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
verl-deepscaler-curated
verl DeepScaleR Curated
A cleaned, de-duplicated and evaluation-safe training split derived from the
DeepScaleR-Preview-Dataset,
reformatted for rule-based-reward RL post-training with
verl.
Total examples: 38,783 (from 41,705 raw records read across three source batches).
Row format
Each row follows the verl dataset_row template:
field
value
data_source
"DeepScaleR"
prompt
[{"role": "user", "content": <problem text>}]
ability
"math"
reward_model… See the full description on the dataset page: https://huggingface.co/datasets/dusersad12/verl-deepscaler-curated.deepscaler-teacher-sft-vllm-official-40k
DeepScaleR teacher SFT vLLM official 40k
Generated run: exp_003_vllm_official_brainlab_2gpu.
Summary
{
"num_examples": 40300,
"sft_dir": "data/processed/deepscaler/teacher_sft/exp_003_vllm_official_brainlab_2gpu",
"parse_rate": 0.9999751861042183,
"correct_rate": 0.5728039702233251,
"format_rate": 0.005955334987593052,
"mean_reward": 0.42432258064534184,
"deepscaler_mean_reward": 0.6266997518610422,
"deepscaler_match_mean_reward":… See the full description on the dataset page: https://huggingface.co/datasets/ThunderstormXXL/deepscaler-teacher-sft-vllm-official-40k.DeepScaleR-Preview-Curated
DeepScaleR-Preview-Curated
DeepScaleR-Preview-Curated is a curated revision of the
agentica-org/DeepScaleR-Preview-Dataset
snapshot used for our Verl (GRPO) math-RL runs.
The published snapshot (226 entries, 220 unique problems) was
reconciled against the maintainer's revision sheet for the next release: retracted problems were dropped, duplicate
uploads were collapsed onto their first occurrence, corrected answers were taken as the authoritative ground truth, and
the… See the full description on the dataset page: https://huggingface.co/datasets/dusersad12/DeepScaleR-Preview-Curated.deepscaler-teacher-sft-vllm-official-40k-clean-v2
DeepScaleR Teacher40k Clean v2
Filtered version of ThunderstormXXL/deepscaler-teacher-sft-vllm-official-40k.
Filtering
minimum official reward: 1.0
maximum text tokens: 8192
maximum response chars: 65000
near-duplicate SimHash hamming threshold: 4
required <think>...</think> and final boxed answer after reasoning
exact text/problem/response dedupe and near problem dedupe
Counts
raw examples: 40300
kept examples: 21727
train examples: 21292
val… See the full description on the dataset page: https://huggingface.co/datasets/ThunderstormXXL/deepscaler-teacher-sft-vllm-official-40k-clean-v2.deepscaler-teacher-sft-vllm-official-40k-clean-v3-no-reward-filter
deepscaler-teacher-sft-vllm-official-40k-clean-v3-no-reward-filter
Filtered version of ThunderstormXXL/deepscaler-teacher-sft-vllm-official-40k.
Filtering
reward filter enabled: False
minimum official reward: 1.0
scoring errors rejected: False
maximum text tokens: 8192
maximum response chars: 65000
near-duplicate SimHash hamming threshold: 4
required <think>...</think> and final boxed answer after reasoning
exact text/problem/response dedupe and near problem… See the full description on the dataset page: https://huggingface.co/datasets/ThunderstormXXL/deepscaler-teacher-sft-vllm-official-40k-clean-v3-no-reward-filter.deepscaler-teacher-sft-vllm-official-40k-clean-v4-conceptual
deepscaler-teacher-sft-vllm-official-40k-clean-v4-conceptual
Filtered version of ThunderstormXXL/deepscaler-teacher-sft-vllm-official-40k.
Filtering
reward filter enabled: False
minimum official reward: 1.0
scoring errors rejected: False
maximum text tokens: 32768
maximum response chars: 200000
near-duplicate SimHash hamming threshold: 4
required <think>...</think> and final boxed answer after reasoning
exact text/problem/response dedupe and near problem dedupe… See the full description on the dataset page: https://huggingface.co/datasets/ThunderstormXXL/deepscaler-teacher-sft-vllm-official-40k-clean-v4-conceptual.DeepScale-qwen2.5_7b-multi_16kmessages是7b模型生成的结果
correct是根据messages最后一个输出的答案进行验证
DeepScaleR-10k_8sampleDeepSaleR数据集中选取1k子集，采样8次
使用模型：qwen2.5-7b-insturct
使用prompt：
ITER_GEN_MULTI_TURN_STEP_PROMPTS = {
"turn0" : "{{ problem }} Let's think step by step and output the final answer within \\boxed{}.",
"turn1" : """{% if correctness %}
3. Since your initial response is self-evaluated as correct, confirm it and provide no further modifications. Put your final answer within \\boxed{}.
{% else %}
3. Since your initial response is self-evaluated as incorrect, there might be an error in the… See the full description on the dataset page: https://huggingface.co/datasets/tttonyyy/DeepScaleR-10k_8sample.