Training LLMs for Code Generation: Data, Evaluation & Best Practices

In today’s AI world, large language models are becoming powerful tools not only for natural language processing but also for automatic code generation. The ability of models to deliver functional software solutions opens new horizons for developers, accelerating development, testing, and integration. However, the effectiveness of such models largely depends on the quality of training data, evaluation methods, and the application of best practices in their creation and use.

A comprehensive approach to preparing software code corpora that includes different programming languages, coding styles, and usage scenarios is important.

Key Takeaways

Supervised fine-tuning (SFT) improves accuracy, readability, and security for coding tasks.
APPS and HumanEval are essential benchmarks for measuring unit-test success and the effort required to repair them.
PEFT/LoRA and QLoRA (NF4) enable fine-tuning on single-GPU setups.

Map user intent and success metrics for code generation LLM training

User Intent	Description	Success Metrics	Metric Explanation
Generate syntactically correct code	User wants code that compiles without errors	Compilability Rate	Percentage of generated code snippets that pass compilation or syntax check
Perform specific functionality	Code should correctly solve the given task	Functional Correctness	Percentage of tests passed by generated code, or probability that at least one of k generated variants is correct
Optimize performance	User expects efficient and fast code	Performance Metrics (Time/Memory)	Execution time and memory usage of generated code compared to a reference solution
Readability and maintainability	Code should be understandable for developers	Code Readability/ Maintainability Scores	Metrics of code style, comments, adherence to best practices
Code security	Generated code should not contain vulnerabilities	Security Vulnerability Detection	Number of potential security issues or unsafe constructs detected
Cross-language generation	Code should be generated in multiple programming languages	Cross-Language Accuracy	Percentage of functionally correct code across different programming languages
Generate specific APIs/libraries	User expects usage of certain APIs or libraries	API Coverage/ Correct Usage	Percentage of correct usage of target APIs or libraries
Creative or alternative solutions	User wants multiple approaches to the same task	Diversity Metrics	Number of distinct working solutions that solve the same task

Building training data pipeline: sources, curation, and labeling

Pipeline Stage	Description	Examples/ Sources	Curation Methods	Labeling Methods
Sources	Initial data for LLM training	Open-source repositories (GitHub, GitLab)	-	-
Curation	Cleaning, normalizing, and filtering data	Removing duplicates, filtering low-quality code, standardizing formatting, removing sensitive/private info	Syntax & semantic validation, style checks, licensing checks	-
Labeling	Adding metadata or categories for training	Functionality tags (sorting, math, API usage), language labels, difficulty levels	-	Manual annotation, semi-automatic labeling (e.g., using test outputs), automated scripts for labeling code features

Dataset design for code LLMs: structure, chunking, and FIM

When designing code training data for an LLM that generates program code, it is important to properly structure and organize the programming dataset. Each dataset should include code spanning multiple programming languages, styles, and levels of complexity to ensure the model's universality. To assess the quality of data and models, it is useful to use standardized benchmarks, such as HumanEval or the MBPP benchmark, which allow for checking code correctness and the model's effectiveness on real programming tasks.

One key aspect is chunking, i.e., breaking large code files or projects into logical blocks. This allows the model to more easily assimilate the code structure, reduces the risk of losing context, and improves training performance. Chunks can be based on functions, classes, modules, or even logical segments of documentation and tests, which provides a more flexible representation of the data.

To improve training efficiency, FIM (Fill-in-the-Middle), an approach that allows the model to restore missing code fragments within existing blocks, is also used. Using FIM improves an LLM's ability to understand internal dependencies between lines of code and generate correct, functional output, which is especially important for code correctness on the HumanEval and MBPP benchmarks.

Model tuning and training stack with PEFT and quantization

The PEFT (Parameter-Efficient Fine-Tuning) approach allows you to adjust some model parameters while preserving the knowledge learned during previous training. For LLMs trained on code, this will be useful, as it allows you to quickly adapt the model to specific programming tasks or new languages without requiring a complete retraining of a large model.

Another key component is quantization, i.e., reducing the precision of model parameters without a significant loss of performance. Using quantization reduces memory requirements and speeds up inference, which is critical for large models working with large programming datasets. Combining PEFT with quantization enables high-performance, resource-efficient code generation.

The training stack for such models typically includes a pre-trained LLM, modules for processing code training data, a pipeline for evaluation on benchmarks such as HumanEval and the MBPP benchmark to assess code correctness, and optimization components such as PEFT and quantization to enable fast, efficient fine-tuning.

Hands-on configuration: from TrainingArguments to reproducibility

Define TrainingArguments, including learning rate, batch size, number of epochs, and weight decay.
Set seed values for reproducibility across PyTorch, NumPy, and random libraries.
Enable gradient accumulation to handle large programming datasets on limited GPU memory.
Configure logging and checkpointing to track training metrics and allow recovery.
Apply mixed-precision (FP16) training to accelerate training and reduce memory usage.
Use an evaluation strategy with metrics such as code correctness and pass rates on the HumanEval and MBPP benchmarks.
Enable distributed training if multiple GPUs or nodes are available.
Apply PEFT or other parameter-efficient fine-tuning methods to reduce training costs.
Save model configuration, tokenizer, and training arguments for exact reproducibility.
Document dataset splits, chunking, and preprocessing steps to ensure consistent results across runs.

Code generation LLM training with SFT

Training LLMs for code generation using SFT (Supervised Fine-Tuning) is a fundamental approach to achieving high accuracy and robustness in models for practical programming problems. In this process, the model first learns from a large code training dataset that spans different programming languages, coding styles, and complexity levels. Then, fine-tuning occurs on specialized programming datasets, where each example consists of an input description of the problem and its corresponding correct solution.

One key aspect of SFT is the use of well-annotated datasets to verify code correctness. To assess the quality of the generated code, standardized benchmarks such as HumanEval and the MBPP benchmark are often used to verify that the model produces functionally correct solutions and adheres to expected behavior.

SFT also enables adapting large language models to specific domains or projects, reducing the risk of errors and improving code-generation efficiency. During training, it is important to control the training parameters, ensure appropriate data chunking, and apply validation on test sets to maintain a high level of code correctness.

Where LLMs fail when generating code: common semantic and syntactic errors

Error Type	Description	Examples	Impact on Code Correctness/Evaluation	Mitigation Strategies
Syntax Errors	Code does not follow programming language rules	Missing colons, unmatched brackets, incorrect indentation	Fails to compile; low pass rates on HumanEval and MBPP benchmark	Use syntax validation during training; include more syntactically correct examples in code training data
Variable/ Scope Errors	Use of undefined or incorrectly scoped variables	Using undefined variables, shadowing issues	Causes runtime errors; fails functional tests	Include examples with proper variable scoping in programming dataset; apply linting
Incorrect Logic/ Semantic Errors	Code is syntactically correct but does not perform the intended task	Off-by-one errors, wrong algorithm, incorrect API usage	Fails functional tests; lowers code correctness scores	Use unit tests and functional tests from HumanEval/ MBPP benchmark; emphasize problem-solving patterns in training data
Incomplete Implementations	Parts of a function or algorithm are missing	Missing return statements, unhandled edge cases	Fails some test cases; partial correctness	Properly chunk code training data; use FIM-style training to fill missing segments
Dependency/ Import Errors	Incorrect library or module imports	Wrong import paths, missing packages	Runtime errors; fails execution	Include diverse examples from real projects in programming dataset; validate dependencies
Performance/ Inefficiency Issues	Code runs but is slow or resource-intensive	Nested loops, inefficient recursion	Low efficiency scores; may fail real-world tasks	Include performance-oriented examples; evaluate on large test inputs

Prompt and instruction design for coding tasks

Prompt Aspect	Description	Example / Implementation	Effect on Code Correctness
Problem Description	Clearly define what the task requires	“Write a function that sorts an array of integers in ascending order”	Helps the model understand the task, reducing semantic errors
Input/Output Examples	Provide input and expected output examples	Input: [3,1,2], Output: [1,2,3]	Guides the model to generate code that passes tests on HumanEval and MBPP benchmark
Constraints & Requirements	Specify limitations or additional requirements	“Do not use external libraries; function must handle empty arrays”	Reduces generation errors and improves code correctness
Style & Formatting	Instructions for coding style	Pythonic style, PEP-8, use functions/classes	Improves readability and maintainability of generated code
Chunked Context	Provide context for code blocks from larger projects	Include related functions or module headers from programming dataset	Reduces errors and improves functional correctness
Structured Templates	Use templates for repetitive tasks	Template: Problem description + Examples + Constraints	Helps LLM align generation with expected outcomes, increasing code correctness

Using RLHF, DPO, and RAG for stronger coding performance

RLHF (Reinforcement Learning from Human Feedback) – learning through reinforcement based on human feedback; the model receives a score for the quality of the generated code and adjusts the generation to increase code correctness and compliance with best practices.
DPO (Direct Preference Optimization) – direct optimization of preferences; the model learns to prefer more correct or desirable code variants based on pairwise comparisons with a programming dataset, without the need for complex reward models.
RAG (Retrieval-Augmented Generation) – generation using retrieval; during code generation, the model refers to external sources, such as code training data or examples from repositories, to improve the accuracy, functionality, and consistency of the results.

FAQ

What is the role of code training data in LLMs for code generation?

Code training data provides examples of real programming tasks that the model learns from. High-quality datasets enhance code correctness and generalization across multiple programming languages.

Why is dataset curation important for programming datasets?

Curation ensures removal of duplicates, low-quality code, and sensitive information while standardizing formatting. This improves model learning efficiency and reduces errors during code generation.

What is chunking, and why is it applied in code LLMs?

Chunking divides large code files into logical segments, such as functions or classes. It allows the model to maintain context and generate syntactically and semantically correct code.

What benefits does FIM (Fill-in-the-Middle) provide for code generation?

FIM trains the model to complete missing segments within existing code blocks. This approach improves code correctness by teaching the model internal dependencies and partial code completion.

What is the purpose of SFT (Supervised Fine-Tuning) in code LLMs?

SFT adapts pretrained LLMs to task-specific programming datasets using input-output examples. It enables the model to generate functional code that passes benchmarks such as HumanEval and the MBPP benchmark.

In what way do prompts and instructions influence code generation?

Well-structured prompts clarify the task, provide input-output examples, and specify constraints. They guide the model to produce code that is syntactically correct and meets code correctness standards.

Which types of failures are common when LLMs generate code?

LLMs often produce syntax errors, incorrect logic, incomplete implementations, or dependency issues. Evaluating outputs on curated programming datasets and standardized benchmarks helps identify and mitigate these errors.

What advantages do PEFT and quantization offer for training code LLMs?

PEFT fine-tunes only a subset of model parameters, and quantization reduces memory requirements. Together, they enable efficient training on large programming datasets while maintaining high code correctness.

What contributions do RLHF, DPO, and RAG make to coding performance?

RLHF aligns outputs with human preferences, DPO optimizes for preferred code choices, and RAG incorporates relevant retrieved code examples. These methods collectively enhance functional accuracy and code correctness in real-world coding tasks.

Why are benchmarks like HumanEval and MBPP essential for evaluation?

They provide standardized tests to measure model performance and code correctness. Using these benchmarks ensures that LLMs generalize effectively across diverse programming datasets and practical coding tasks.