concretejungles/T-Wix-instag

Note This is a derivative of t-tech/T-Wix. The only change is an added supertag string column (InsTag-style coarse category of the first user turn). Taxonomy version: 2026-04-28. Allowed values: math, code, science, general-instruction, general-knowledge, writing. All other columns, rows, and splits are identical to the upstream dataset. Tags were produced by embedding prompts with Qwen/Qwen3-Embedding-4B, clustering with UMAP+HDBSCAN, and naming clusters with Qwen/Qwen3.6-35B-A3B.

Resulting supertag distribution:

• general-knowledge: 127,164 rows (25.45%)
• code: 121,686 rows (24.36%)
• math: 84,378 rows (16.89%)
• science: 72,781 rows (14.57%)
• writing: 58,671 rows (11.74%)
• general-instruction: 34,918 rows (6.99%)

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T-Wix SFT Mixture

🚨 T-Wix is built entirely from publicly available data and intended for use in research and development. The dataset may contain noise, biases, or artifacts that require careful inspection and preprocessing. Users are fully responsible for any downstream use and must ensure compliance with ethical, legal, and safety standards.

📝 Dataset Summary

T‑Wix is a Russian supervised fine‑tuning (SFT) dataset.

The dataset is divided into 2 sticks:

1. General (468614 samples) — Covering a broad range of topics, including Math, Science, Coding & Programming, General Knowledge, Instruction Following, Roleplay, and more.
2. Reasoning (30984 samples) — Focusing on advanced math and science problems with detailed reasoning traces.

It combines a variety of prompts drawn from open‑source resources and high‑quality Russian translations of English datasets. The primary goal is to enhance the model’s core capabilities — ranging from solving algorithmic and mathematical problems to dialogue, logical thinking, and reasoning mode.

We also add long context, including summarization and long-form question–answer pairs, with contexts up to 32000 tokens, and corpus of samples in English in the General part.

The total size of the dataset is about 499598 samples in Russian.

⚙️ Data Preparation

To ensure high quality, variety and thematic diversity, we applied a multi-stage filtering pipeline for General and Reasoning data.

General Data

• Stage 0: Deduplication. We removed near‑duplicates using locality‑sensitive hashing (LSH) and embedding‑based similarity.
• Stage 1: Diversity Control with #InsTag. We used Instruction Tagging #InsTag to balance themes and styles, preventing any single topic from dominating the data.
• Stage 2: Quality Filtering. A reward model (RM‑score) evaluated each sample, eliminating low‑quality examples.
• Stage 3: Difficulty Selection. We used Instruction-Following Difficulty (IFD) to retain the most challenging examples.
• Stage 4: Rejection Sampling. Finally, we generated 8 completions for each prompt with more capable model and choose the best one by RM-score.

Reasoning Data

• Stage 0: Deduplication. Same as for General data, we removed near-duplicates using LSH and embedding similarity.
• Stage 1: Prompt-Level Filtering via RM-Score Distributions. For each prompt, 8 completions were generated using a teacher model and 8 using a base model. Each of them was scored using a reward model and normalized using softmax. We discarded prompts with overly low or high median scores.
• Stage 2: KL-Based Selection. We computed KL divergence between the teacher and student RM-score distributions and retained prompts with mid-range KL values. This allowed us to select examples that were balanced in complexity.
• Stage 3: Final Completion Selection. For verifiable prompts, correctness was checked. Final answers were selected as the shortest among the top-3 teacher completions ranked by RM-score - balancing quality and conciseness.

Responses were generated with more capable models — such as DeepSeek‑V3‑0324 and Qwen3-235B-A22B, to ensure high accuracy and relevance.

Token counts were computed using tiktoken with o200k_base tokenizer.

📌 Data fields

• id(str) — unique ID of sample.
• messages(list) — an array of messages, where each message includes a role (system, user or assistant) and content.
• subset(str) — general, reasoning, long_context or english_corpus subsets.

🔐 License

This dataset is licensed under ODC-BY-1.0. It includes outputs from third-party models, which may be subject to separate terms. See each subset link for specific licensing details.

📚 Sources

• BAAI/Infinity-Instruct
• rombodawg/Everything_Instruct
• H-D-T/Buzz-V1.2
• nyuuzyou/chatgpt-in-russia-qa
• nyuuzyou/ruschatgpt-qa
• lmsys/lmsys-chat-1m
• Replete-AI/code_bagel
• BAAI/TACO
• HuggingFaceTB/smoltalk
• allenai/WildChat
• allenai/tulu-3-sft-mixture
• arcee-ai/The-Tome
• AI-MO/NuminaMath-CoT
• RussianNLP/Mixed-Summarization-Dataset
• nvidia/Daring-Anteater
• stanfordnlp/SHP
• Locutusque/hercules-v6.1
• HuggingFaceH4/no_robots
• meta-math/MetaMathQA
• teknium/OpenHermes-2.5
• OpenAssistant/oasst2
• nvidia/HelpSteer2
• openbmb/UltraFeedback
• argilla/Capybara-Preferences
• m-a-p/CodeFeedback-Filtered-Instruction
• Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Llama3
• https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm
• TIGER-Lab/WebInstruct-verified
• nvidia/AceReason-Math
• nvidia/Llama-Nemotron-Post-Training-Dataset
• EricLu/SCP-116K
• open-r1/OpenR1-Math-220k
• open-r1/OpenThoughts-114k-math
• open-r1/codeforces-cots
• LLM360/guru-RL-92k
• KodCode/KodCode-V1-SFT-R1

📖 Citation

If you find our work useful in your research, please consider citing the following paper:

@inproceedings{stoianov-etal-2026-pro,
    title = "{T}-pro 2.0: An Efficient {R}ussian Hybrid-Reasoning Model and Playground",
    author = "Stoianov, Dmitrii  and
      Taranets, Danil  and
      Tsymboi, Olga  and
      Latypov, Ramil  and
      Dautov, Almaz  and
      Kruglikov, Vladislav  and
      Nikita, Surkov  and
      Abramov, German  and
      Gein, Pavel  and
      Abulkhanov, Dmitry  and
      Gashkov, Mikhail  and
      Zelenkovskiy, Viktor  and
      Batalov, Artem  and
      Medvedev, Aleksandr  and
      Potapov, Anatolii",
    editor = "Croce, Danilo  and
      Leidner, Jochen  and
      Moosavi, Nafise Sadat",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.eacl-demo.22/",
    doi = "10.18653/v1/2026.eacl-demo.22",
    pages = "297--319",
    ISBN = "979-8-89176-382-1"
   }