metadata

license: apache-2.0
task_categories:
  - question-answering
language:
  - ru
pretty_name: T-math
size_categories:
  - n<1K
dataset_info:
  features:
    - name: question
      dtype: string
    - name: verifiable_answer
      dtype: string
    - name: year
      dtype: string
    - name: grade
      dtype: string
    - name: full_answer
      dtype: string
    - name: solutions
      list: string
    - name: task_complexity
      dtype: string
    - name: olympiad
      dtype: string
  splits:
    - name: train
      num_bytes: 510955
      num_examples: 331
  download_size: 228445
  dataset_size: 510955
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

🧮 T-Math

T-Math is a dataset of Russian math olympiad problems created to assess the reasoning capabilities of large language models (LLMs) in mathematics.
It includes 331 problems from the All-Russian School Olympiad and the Moscow Olympiad for high school students, covering the period from 1998 to 2025.
The tasks and their ground-truth answers were extracted automatically and subsequently verified by human assessors.

Key features:

Challenging problems that require multi-step reasoning (median completion length for Qwen3-32B is 16K tokens), sourced from top-tier Russian olympiads
Easily verifiable: answers are numeric-only and checked using the math_verify library to compare mathematical expressions
Not yet saturated, even by frontier reasoning models such as Gemini 2.5 Pro and DeepSeek R1
Contains 331 samples — the largest Russian math olympiad-level benchmark — making it more statistically robust compared to smaller datasets like the 30-sample AIME benchmark

📊 Evaluation Results

Model	pass@1
o4-mini-high	0.73
DeepSeek-R1-0528	0.71
Gemini-2.5-Pro	0.70
Claude Sonnet 4	0.56
T-pro-it-2.0	0.54
Qwen3-32B	0.53

🗂️ Filtering procedure

The text was extracted from PDFs using Qwen/Qwen2.5-VL-72B-Instruct. Tasks, along with their ground-truth and verifiable (numeric) answers, were extracted via LLM calls. We filtered out invalid questions using an LLM based on the following criteria:

Tasks requiring multiple answers
Tasks without a single correct answer
Theorem-like tasks where the main goal is proving a statement, making automatic verification non-trivial
Tasks with non-numeric answers, to simplify answer comparison
Tasks that cannot be solved without access to an accompanying image

Next, we removed tasks of moderate difficulty where Qwen3-8B achieved a 100% pass@16 rate, as they offer limited value for benchmarking reasoning. Finally, both the questions and the verifiable answers were manually reviewed by assessors to ensure consistency with the original sources.

🛠️ How to use

Add the following system prompt to guide the model to return the final answer in a \boxed{} tag, making it easier to parse:

Решите следующую математическую задачу эффективно и ясно. Последняя строка вашего ответа должна иметь следующий формат:
'Таким образом, окончательный ответ: $\boxed{ОТВЕТ}$.' (без кавычек), где ОТВЕТ - это просто окончательное число или выражение, решающее задачу.
Думайте шаг за шагом перед ответом.

You can then use the following code snippet with the math_verify library to compare mathematical expressions:

from math_verify import LatexExtractionConfig, parse, verify
from latex2sympy2_extended import NormalizationConfig


def accuracy_reward(completion: str, solution: str) -> float:
    """Reward function that checks if the completion matches the ground truth."""
    # parse the gold solution (assumed to always succeed)
    gold_parsed = parse(solution, extraction_mode="first_match")

    # parse the model’s completion with the same LaTeX extraction settings
    answer_parsed = parse(
        completion,
        extraction_config=[
            LatexExtractionConfig(
                normalization_config=NormalizationConfig(
                    nits=False,
                    malformed_operators=False,
                    basic_latex=True,
                    equations=True,
                    boxed="all",
                    units=True,
                )
            )
        ],
        extraction_mode="first_match",
    )

    # verify and return binary reward; on error, print and give 0.0
    try:
        return float(verify(gold_parsed, answer_parsed))
    except Exception as e:
        print(f"verify failed: {e}, answer: {answer_parsed}, gold: {gold_parsed}")
        return 0.0