SLAI-AITP/SLAI-T-Rex-SFT

SLAI T-Rex OR SFT Dataset

The SLAI T-Rex OR SFT Dataset is a solver-oriented supervised fine-tuning corpus for Operations Research (OR) and mathematical programming. It was created with the self-distillation, contract-aware cleaning, review, and validation pipeline described in the SLAI T-Rex technical report.

• Paper: SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD
• Paper PDF: arXiv:2607.20145
• Data-construction code: SLAI-AITP/SLAI-T-Rex

Dataset Summary

Item	Description
Domain	Operations Research and mathematical programming
Release size	8,881 validated SFT records
Source pool	10,000 Clean-CoT candidates
Languages	Primarily English
Problem representations	DP, DT, and DPS
Solver ecosystem	Gurobi-style Python modeling, primarily aligned with gurobipy 12.x
Common response forms	Tagged reasoning/model/code, code-only answers, and LP-writing tasks

The report refers to the selected training configuration as the 10K Clean-CoT dataset. More precisely, 10,000 candidates entered the cleaning workflow, 9,174 passed reviewer filtering, and 8,881 passed the subsequent validation and export gates. This repository releases the final 8,881 validated records.

Dataset Construction

The data pipeline starts from high-quality optimization-modeling seeds and uses three intermediate representations:

1. L1 Canonical IR normalizes the source, target contract, data interface, static code properties, and risk labels.
2. L2 Semantic IR extracts objectives, sets, parameters, variables, constraints, and modeling risks.
3. L3 Synthetic IR creates a new task abstraction with different domains, entities, numerical values, schemas, and modeling structures rather than copying the seed.

Each accepted task is rendered into one of three problem representations:

• DP (Data in Problem): all data are written directly in the problem statement.
• DT (Data in Table): data are represented in structured tables, usually Markdown tables.
• DPS (Data-Problem Separate): the task uses a separate structured data interface, such as JSON or CSV.

Answer rendering follows the task contract. Tagged mathematical-modeling answers commonly contain <think>, <model>, and <python> blocks. Code-only feasibility tasks and LP-structure tasks retain stricter output formats.

Quality Control

The final release is produced through four quality gates:

1. Cleaner: repairs concrete modeling, implementation, or output-contract errors.
2. Reviewer: checks whether the repair is justified and blocks unsupported changes or over-repair.
3. Local validation: checks code execution, Gurobi usage, output contracts, and LP-writing behavior where applicable.
4. Export validation: verifies that only accepted, correctly formatted samples enter the release.

The Clean-CoT branch adds concise, task-relevant modeling checks for variable domains, objective direction, core constraints, nonlinear or ratio forms, and solver actions. It is intended to improve the correctness and learnability of the target response, not to maximize reasoning length.

The pipeline also applies text- and IR-level similarity controls to reduce direct reuse of benchmark wording, numerical values, objective semantics, constraint patterns, and parameter schemas. These controls reduce leakage risk but do not constitute a formal proof of decontamination.

Intended Use

The dataset is intended for:

• supervised fine-tuning of mathematical-modeling and OR assistants;
• research on domain-specific CPT/SFT transfer;
• output-contract and solver-code alignment;
• data-quality, structural-verification, and decontamination studies;
• controlled evaluation of modeling and solver-facing behavior.

The dataset is not an evaluation benchmark. Users should keep evaluation instances and derived variants out of training splits and perform task-appropriate decontamination checks.

Citation

If you use this dataset, please cite the technical report:

@misc{li2026slaitrexfullparameterposttraining,
  title        = {SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD},
  author       = {Dongfang Li and others},
  year         = {2026},
  eprint       = {2607.20145},
  archivePrefix= {arXiv},
  url          = {https://arxiv.org/abs/2607.20145}
}