mlsanityData to 14 Oct 2026

TACO: Topics in Algorithmic COde generation dataset

arXiv 2312.14852 ↗First posted 22 Dec 2023cs.AIcited by 160

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Cited by 68 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce TACO, an open-source, large-scale code generation dataset, with a focus on the optics of algorithms, designed to provide a more challenging training dataset and evaluation benchmark in the field of code generation models. TACO includes competition-level programming questions that are more challenging, to enhance or evaluate problem understanding and reasoning abilities in real-world programming scenarios. There are 25433 and 1000 coding problems in training and test set, as well as up to 1.55 million diverse solution answers. Moreover, each TACO problem includes several fine-grained labels such as task topics, algorithms, programming skills, and difficulty levels, providing a more precise reference for the training and evaluation of code generation models. The dataset and evaluation scripts are available on Hugging Face Hub (https://huggingface.co/datasets/BAAI/TACO) and Github (https://github.com/FlagOpen/TACO).

Key facts. Cited by 160 papers in the map (data through 14 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 22 Dec 2023.

Builds on

11 of 15 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2021Program Synthesis with Large Language Modelscited by 3,637
  6. 2023Code Llama: Open Foundation Models for Codecited by 2,139
  7. 2022Competition-Level Code Generation with AlphaCodecited by 1,462
  8. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  9. 2021Measuring Coding Challenge Competence With APPScited by 902
  10. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 885
  11. 2023StarCoder: may the source be with you!cited by 799

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 19; 2025: 66; 2026: 75.

160 papers.

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  2. 2026GLM-5: from Vibe Coding to Agentic Engineeringcited by 587
  3. 2025Process Reinforcement through Implicit Rewardscited by 504
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  7. 2025Skywork Open Reasoner 1 Technical Reportcited by 165
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  10. 2025KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Codingcited by 119
  11. 2025CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratingscited by 115
  12. 2025OpenCodeReasoning: Advancing Data Distillation for Competitive Codingcited by 113
38 more
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  3. 2025ACECODER: Acing Coder RL via Automated Test-Case Synthesiscited by 93
  4. 2024o1-Coder: an o1 Replication for Codingcited by 78
  5. 2025AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergycited by 77
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  8. 2025LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMscited by 70
  9. 2024EffiBench: Benchmarking the Efficiency of Automatically Generated Codecited by 64
  10. 2025OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMscited by 60
  11. 2025rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Datasetcited by 51
  12. 2025Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Modelscited by 48
  13. 2025CodeContests+: High-Quality Test Case Generation for Competitive Programmingcited by 46
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  16. 2025Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Trainingcited by 42
  17. 2025Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving Clipping Policy Optimizationcited by 39
  18. 2025QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generationcited by 35
  19. 2024CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verificationcited by 34
  20. 2025Scalable Chain of Thoughts via Elastic Reasoningcited by 34
  21. 2025Dynamic Scaling of Unit Tests for Code Reward Modelingcited by 33
  22. 2024CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?cited by 30
  23. 2025100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Modelscited by 30
  24. 2025OJBench: A Competition Level Code Benchmark For Large Language Modelscited by 26
  25. 2025OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critiquecited by 25
  26. 2025BroRL: Scaling Reinforcement Learning via Broadened Explorationcited by 25
  27. 2024PyBench: Evaluating LLM Agent on various real-world coding taskscited by 24
  28. 2024Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalizationcited by 24
  29. 2025CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignmentcited by 24
  30. 2026Rethinking On-Policy Distillation of Large Language Models II: One Training Examplecited by 24
  31. 2024CodeEditorBench: Evaluating Code Editing Capability of Large Language Modelscited by 23
  32. 2025HardTests: Synthesizing High-Quality Test Cases for LLM Codingcited by 23
  33. 2025RL Grokking Recipe: How Does RL Unlock and Transfer New Algorithms in LLMs?cited by 23
  34. 2026RAGEN-2: Reasoning Collapse in Agentic RLcited by 20
  35. 2024Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Modelscited by 19
  36. 2024Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMscited by 17
  37. 2024Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimizationcited by 17
  38. 2025Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learningcited by 17

50 most cited of 160 All 160 (JSON)

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  3. 2025TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection
  4. 2025How to Get Your LLM to Generate Challenging Problems for Evaluation
  5. 2022TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data
  6. 2026CodeEval: A pedagogical approach for targeted evaluation of code-trained Large Language Models
  7. 2025QueST: Incentivizing LLMs to Generate Difficult Problems
  8. 2025TACO: Think-Answer Consistency for Optimized Long-Chain Reasoning and Efficient Data Learning via Reinforcement Learning in LVLMs