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[Agents]·PAP-6HBRQV·2021·March 17, 2026·Free Preview

Evaluating Large Language Models Trained on Code

2021

Mark Chen, Jerry Tworek, Heewoo Jun et al.

AGENTS

4 min readAgentsTool Use

Core Insight

Codex rewrites the future of code with a 70.2% success rate, leaving GPT-3's 0% in the dust.

By the Numbers

70.2%

success rate with 100 samples

GPT-3's success rate

28.8%

success rate with a single attempt

164

number of HumanEval challenges

In Plain English

Codex, a GPT model fine-tuned on GitHub code, solves 70.2% of Python tasks with multiple samples. This dwarfs GPT-3’s 0% performance.

Knowledge Prerequisites

git blame for knowledge

To fully understand Evaluating Large Language Models Trained on Code, trace this dependency chain first. Papers in our library are linked — click to read them.

DIRECT PREREQIN LIBRARY

Attention Is All You Need

This paper introduces the transformer architecture, a foundational framework for understanding how language models, including those trained on code, process sequential data.

transformer architectureself-attentionpositional encodings

DIRECT PREREQIN LIBRARY

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Understanding BERT's architecture and bidirectional training is essential for grasping the evolution of large language models and their training methodologies.

bidirectional transformerpre-trainingmasked language model

DIRECT PREREQIN LIBRARY

Training language models to follow instructions with human feedback

This paper discusses techniques for training models with human feedback, which is crucial for understanding how models handle tasks like code generation.

instruction tuninghuman feedbacklanguage model fine-tuning

DIRECT PREREQIN LIBRARY

GPT-4 Technical Report

The technical report on GPT-4 provides insights into large-scale language models, which is necessary for understanding advancements in models trained on code.

GPT architecturescaling lawsfew-shot learning

DIRECT PREREQIN LIBRARY

Competition-Level Code Generation with AlphaCode

This paper offers a detailed example of a language model specifically focused on code generation, a crucial aspect for evaluating language models trained on code.

code synthesislanguage model evaluationprogramming competitions

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Evaluating Large Language Models Trained on Code

The Idea Graph

⚠Problem✦Insight⬡Method◎Result→Impact

10 nodes · 9 edges

Click a node to explore · Drag to pan · Scroll to zoom

319 words · 2 min read6 sections · 10 concepts

The Problem: GPT-3 Limitations in Code

49 words

Before the introduction of Codex, GPT-3, a prominent language model, struggled with programming tasks. It managed to solve 0% of Python challenges, highlighting its limitations in understanding programming languages. This failure stems from its training on generalized language data, which lacks the specific syntax and structure of coding languages.

Key Insight: Codex Breakthrough

53 words

The key insight behind the paper is the development of Codex, a breakthrough in language models fine-tuned specifically on code. Codex's success lies in its targeted training on programming languages, allowing it to achieve a 70.2% success rate on Python tasks. This insight demonstrates the power of specialization over generalization in AI training.

Method: Domain-Specific Training and Fine-Tuning with GitHub

44 words

Codex was developed by employing , focusing on data from GitHub repositories. By fine-tuning the model specifically with code, it learned the syntax and nuances of programming languages. This approach allowed Codex to significantly surpass the performance of general language models like GPT-3.

Method: HumanEval Benchmark

45 words

To rigorously evaluate Codex, researchers used the , a set of 164 hand-written programming challenges. This benchmark provided a robust framework for testing the practical coding abilities of AI models, ensuring that Codex's performance was not just theoretical but applicable to real programming scenarios.

Results: Codex Success Rate and Contrast to GPT-3

54 words

Codex achieved remarkable results, solving 28.8% of problems on its first attempt and 70.2% with 100 samples per task. These results are in stark 's 0% success rate, underscoring the effectiveness of Codex's domain-specific training approach. This contrast highlights the importance of training models with data that matches their intended application domain.

Impact: Transforming Developer Tools and the Future of Coding

74 words

Codex's success has profound implications for developer tools and the . Its ability to generate functional code enhances tools like IDEs and code-assist plugins, making AI-assisted coding more prevalent. , powered by Codex, exemplifies this shift, lowering the entry barrier for programming and boosting productivity across the tech industry. This transformation suggests a future where AI is integral to both learning and executing programming tasks, fostering innovation and accelerating development cycles.

Experience It

Live Experiment

Codex Fine-Tuning

See Codex's Code Mastery in Action

You will see how Codex, fine-tuned on GitHub code, dramatically outperforms a general-purpose language model in solving programming tasks.

Notice how Codex's specialized training on code allows it to solve programming tasks with high accuracy, unlike the general model which struggles with syntax and logic.

Try an example — see the difference instantly

Enter a Python programming task — or try your own

⌘↵ to run

Read Original Paper on arXiv

Origin Story

arXiv preprintOpenAIMark Chen, Jerry Tworek et al.

The Room

At OpenAI, a small group of researchers sits hunched over keyboards, cups of coffee cooling beside them. The lab hums with the faint buzz of computers, a sound that mirrors the electric tension in the air. They are puzzled by GPT-3's limitations with code, wondering if there’s a way to teach a machine the nuances of programming languages.

The Bet

While others believed language models were for text, this team gambled on the idea that code is just another language. They trained models on vast reams of code, hoping to unlock programming potential. There were moments of doubt, especially when early models failed to compile simple snippets. Yet they pressed on, convinced that code could be cracked by the same abstractions that worked for prose.

The Blast Radius

Without this paper, Copilot might still be a sci-fi dream. Software development workflows would be less efficient, lacking the AI-driven assistance that has become standard. Key authors found themselves at the forefront of AI-assisted programming, some continuing at OpenAI, others influencing tech across the globe.

↳Copilot↳ChatGPT Code Interpreter

Explained Through an Analogy

“

Codex is like a magic pen for programmers, transforming vague ideas into robust code with the flair of a skilled artisan. It doesn’t just write; it anticipates the next brushstroke, turning a sketch into a masterpiece.

The Full Story

~1 min · 220 words

The Context

What problem were they solving?

odex fine-tunes GPT on GitHub code to enhance its programming abilities, unlike standard GPT models.

The Breakthrough

What did they actually do?

HumanEval set tests AI with 164 Python coding tasks, offering rigor in assessing code-writing capabilities.

Under the Hood

How does it work?

Codex's coding success reveals importance of domain-specific data over general language models.

World & Industry Impact

Codex's ability to generate functional code with impressive accuracy shakes up developer tools, such as IDEs and code-assist plugins. GitHub Copilot, powered by Codex, signifies a new era where AI-assisted coding becomes a norm, improving productivity across tech giants like Microsoft, Google, and general software development platforms. By lowering the barrier of entry for coding, Codex can transform how programming is learned and executed, fostering innovation and accelerating development cycles.

Highlighted Passages

Verbatim lines from the paper — the sentences that carry the most weight.

“Codex, a GPT model fine-tuned on GitHub code, solves 70.2% of Python tasks with multiple samples.”
→ This highlights Codex's substantial improvement over GPT-3, showcasing the power of domain-specific fine-tuning.

“The novelty of this approach lies in tailoring the model's training data specifically to the structure and syntax of programming languages.”
→ Understanding the impact of training data specificity is crucial for PMs looking to leverage AI for specialized applications.

“Codex's ability to generate functional code with impressive accuracy shakes up developer tools, such as IDEs and code-assist plugins.”
→ This emphasizes the transformative potential of Codex in redefining software development processes and tools.

How grounded is this content?

Metrics are computed from available source text only — abstract, summary, and impact fields ingested into this system. Full paper PDF is not ingested; numerical claims that originate from within the paper body will not appear in these scores.

Source Richness100%

8 of 8 content fields populated. More fields = better-grounded generation.

Source Depth~249 words

Total source text analyzed by the model. Includes extended deep-dive summary — high confidence.

Number Grounding4 / 4

Key statistics whose numeric values appear verbatim in ingested source text. Unverified stats may originate from the full paper body.

Quote Traceability3 / 3

Key passages whose significant vocabulary (≥4-char words) overlap ≥35% with source text. Measures lexical traceability, not semantic accuracy.

Methodology: Number grounding uses regex digit extraction against source text. Quote traceability uses token set intersection on content words stripped of stop-words. Neither metric validates semantic correctness or factual accuracy against the original paper. For full verification, cross-reference with the original paper via the arXiv link above.

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context Emergent Abilities of Large Language Models

Evaluating Large Language Models Trained on Code

Table of Contents

The Problem: GPT-3 Limitations in Code

Key Insight: Codex Breakthrough

Method: Domain-Specific Training and Fine-Tuning with GitHub

Method: HumanEval Benchmark

Results: Codex Success Rate and Contrast to GPT-3

Impact: Transforming Developer Tools and the Future of Coding

See Codex's Code Mastery in Action

The Context

The Breakthrough

Under the Hood

The Problem

GPT-3's Limitation

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