To Code or Not To Code? Exploring Impact of Code in Pre-training

## Abstract

Abstract Including code in the pre-training data mixture, even for models not specifically designed for code, has become a common practice in LLMs pre-training. While there has been anecdotal consensus among practitioners that code data plays a vital role in general LLMs’ performance, there is only limited work analyzing the precise impact of code on non-code tasks. In this work, we systematically investigate the impact of code data on general performance. We ask “ what is the impact of code data used in pre-training on a large variety of downstream tasks beyond code generation ”. We conduct extensive ablations and evaluate across a broad range of natural language reasoning tasks, world knowledge tasks, code benchmarks, and LLM-as-a-judge win-rates for models with sizes ranging from 470M to 2.8B parameters. Across settings, we find a consistent results that code is a critical building block for generalization far beyond coding tasks and improvements to code quality have an outsized impact across all tasks. In particular, compared to text-only pre-training, the addition of code results in up to relative increase of 8.2% in natural language (NL) reasoning, 4.2% in world knowledge, 6.6% improvement in generative win-rates, and a 12x boost in code performance respectively. Our work suggests investments in code quality and preserving code during pre-training have positive impacts.

## 1 Introduction

The role of data has taken on critical significance in recent breakthroughs. State-of-the-art models highlight the importance of the pre-training data mixture, diversity of data sources [Brown et al., ; Longpre et al., ; Singh et al., ] combined with compute availability as key drivers on performance [Dubey et al., ; Üstün et al., ; Team et al., ; Aryabumi et al., ; DeepSeek-AI, ]. A critical question is what properties of data impart the best general performance?

Perhaps surprisingly, code is often included in pre-training even if a model is not explicitly intended to generate high-quality code. Code datasets differ significantly in terms of structure and textural characteristics from high-quality web datasets [; Raffel et al., ]. Despite this, several previous generations of LLMs like PaLM [Chowdhery et al., ], Gopher [Rae et al., ] and Bloom [Workshop et al., ] that were not explicitly intended to support code generation, included a percentage of code data together with high-quality natural language data in their pre-training mixture.

In current state-of-the-art models, it has become an accepted norm to not only include code data but futher increase the proportion of code – for instance, Llama 3 [Dubey et al., ] has four times more code data in proportion, of its pre-training mixture than Llama 2 [Touvron et al., ] (17% for Llama 3 vs 4.5% for Llama 2). While there has been consensus anecdotally among practitioners that code data plays a vital role in LLMs’ performance, there has been only limited work analyzing the precise impact of code on non-code tasks. Previous work shows particular side benefits of the inclusion of code data, such as impact on scaling in limited data regime [Muennighoff et al., ], entity tracking capabilities [Kim et al., ], and mathematical reasoning []. However, there has been no exhaustive study to date that systematically investigates the impact of code data on general performance. In this work, we ask ‘‘what is the impact of code data used in pre-training on a large variety of downstream tasks beyond code generation’’.

We embark on an exhaustive set of large-scale controlled pre-training experiments. This includes a consideration of where in the training process adding code is beneficial, code proportions, the role of scaling, and the quality and properties of code added. While a costly endeavor to perform these ablations in a rigorous way, we find consistent and valuable results that code provides critical improvements to non-code performance. In particular, compared to text-only pre-training, for our best variant, the addition of code results in relative increase of 8.2% in natural language (NL) reasoning (NL) , 4.2% in world knowledge, 6.6% improvement in generative win-rates, and a 12x boost in code performance respectively. Further performing cooldown with code, improves NL reasoning by 3.7%, World knowledge by 6.8%, and code by 20%, relative to cooldown without code and leads to a 4.1% additional win-rate increase.

Here, several factors matter including getting the proportion of code correct, improving the quality of code by including synthetic code and code adjacent data such as commits, and leveraging code across multiple stages of training including during cooldown. Our results suggest code is a critical building block for generalization far beyond coding tasks and improvements to code quality have an outsized impact on performance. We conduct an extensive evaluation on a broad range of benchmarks, which cover world knowledge tasks, natural language reasoning, and code generation, as well as LLM-as-a-judge win-rates. Across experiments on models ranging from 470 million to 2.8 billion parameter models, we find the following detailed results:

- 1.
Code provides critical improvements to non-code performance.
Initialization with code pre-trained models results in improved performance for natural language tasks. In particular, compared to text-only pre-training, for our best variant, the addition of code results in a relative increase of 8.2% in NL reasoning, 4.2% in world knowledge, 6.6% improvement in generative win-rates, and a 12x boost in code performance respectively.
- 2.
Code quality and properties matter. Using markup-style programming languages, code-adjacent datasets such as GitHub commits and synthetically generated code improves the performance in pre-training. In particular, training on a higher quality synthetically generated code dataset results in a 9% and 44% increase in natural language reasoning and code performance, respectively, compared to web-based code data in pre-training. Additionally, continual pre-training from a code model that includes synthetic data results in 1.9% and 41% relative increases in natural language reasoning and code performance respectively, compared to initialization from a code model that does not include code data.
- 3.
Code in cooldown enables further improvement across all tasks. Including code data in pre-training cooldown, where high-quality datasets are up-weighted, leads to an increase of 3.6% in NL reasoning, 10.1% in world knowledge, and 20% in code performance relative to the model before cooldown. More significantly, cooldown with code beats the baseline (model without cooldown) by 52.3% win-rates, where win-rates are 4.1% higher compared to cooldown without code.

## 2 Methodology

We describe the details of our Pre-training Data (Section 2.1), Evaluation (Section 2.2), Training and Model details (Section 2.3). Figure 1 shows the high-level experimental framework. Precise details for each experiment and their results are presented in Section 3.

Figure: Рис. 1: Overview of our experimental framework: We exhaustively evaluate the impact of code by varying: 1) the proportion of code in pre-training, 2) code quality and properties, 3) model initialization, 4) model scale, and 5) stage of training at which code is introduced. We evaluate the resulting model on a wide-ranging set of tasks, including natural language reasoning, world knowledge, code, and open-ended generations.
Refer to caption: x1.png

### 2.1 Pre-training Data

In this section, we describe the details of our pre-training and cooldown datasets. We aim to evaluate the role of code in pre-training, following current state-of-art practices. Hence, we consider pre-training runs that consist of two phases: 1) continued pretraining and 2) cooldown. Continued pre-training refers to training a model that is initialized from a pre-trained model and trained for a fixed token budget. Cooldown [Team et al., ; Parmar et al., ] involves up-weighting high-quality datasets and annealing the learning rate for a relatively small number of tokens during the final stages of training. This up-weighting of high-quality datasets for a smaller amount of steps at the end of training can significantly boost model quality.

Text dataset. We use the SlimPajama pre-training corpus [Soboleva et al., ] as our source of natural language text data. SlimPajama is a de-duplicated, quality-filtered, multi-corpora, open-source dataset based on RedPajama-1.2T [Computer, ]. SlimPajama consists of documents from CommonCrawl, C4, GitHub, Books, ArXiv, Wikipedia, and StackExchange. We filter out all documents from GitHub and StackExchange to remove code and code-adjacent data sources and ensure this is a text-only source. SlimPajama has a total of 627B tokens. After removing all code sources, this results in our text pre-training corpus with a total of 503B tokens.

Code datasets. To explore the impact of different properties of code data, we use multiple sources of code in our experiments:

- •
Web-based Code Data: For our main source of code data, we start with the Stack dataset [Kocetkov et al., ] that was used to train StarCoder [Li et al., ]. The Stack consists of permissively licensed code data scraped from GitHub. We apply quality filters(^1^11See Appendix A.1 for details about quality filters) and restrict to the top 25 programming languages based on document count (^2^22Refer to Appendix A.2, A.3 for the full list of programming and markup-style languages included). After all filtering steps, the size of the code-only and markup subset is 139B tokens.
- •
Markdown data We also separately process markup-style languages such as Markdown, CSS, and HTML (^2^2footnotemark: 2). After all filtering steps, the size of this markup subset is 180B tokens.
- •
Synthetic Code Data: To ablate the quality of the code dataset, we use a proprietary synthetically generated code dataset that consists of Python programming problems that have been formally verified. We treat this as a high-quality source of code data (See the details in Section 3.4). The final synthetic dataset consists of 3.2B code tokens.
- •
Code Adjacent Data: Finally, to explore different properties of code data, we include a version of the code data which includes auxiliary data such as GitHub commits, jupyter notebooks, StackExchange threads. For GitHub commits, and jupyter notebooks we use the datasets provided as part of the Stack [Kocetkov et al., ].
We use the version of StackExchange that is part of SlimPajama [Soboleva et al., ]. In total we have 21.4B tokens of code-adjacent data.

Pre-training cooldown datasets. Cooldown involves up-weighting higher quality datasets for the final steps of pre-training. It has been found by recent work to improve performance on downstream tasks [Parmar et al., ; Team et al., ], in particular on helping impart instruction-following capabilities. We choose a pre-training cooldown mixture comprising high-quality text, math, code, and instruct-style text datasets.

### 2.2 Evaluation

Our goal is to systematically understand the impact of code on general performance, which requires a broad evaluation suite that extends to a large variety of downstream tasks beyond code generation. To achieve this, we evaluate models on benchmarks that are reasonable proxies for model ability on 1) world knowledge, 2) natural language reasoning, and 3) code performance. In addition, we report win-rates as evaluated by an LLM-as-a-judge. Table 2.2 shows the full evaluation suite and their respective grouping, along with the metric used.

We briefly describe the composite datasets used for each category below:

- 1.
World knowledge. These benchmarks aim to measure world knowledge, testing knowledge memorization, retrieval, and question answering capability given context. We include Natural Questions Open [Kwiatkowski et al., ], and TriviaQA [Joshi et al., ] as the datasets. We report the average exact match scores for both these benchmarks.
- 2.
Natural language reasoning. The Natural language (NL) reasoning suite consists of 11 benchmarks that involve natural language based reasoning such as Question Answering [Clark et al., ; Seo et al., ; Welbl et al., ; Sap et al., ; Choi et al., ], natural language inference (NLI) [Wang et al., ; de Marneffe et al., ; Wang et al., ], sentence completion [Mostafazadeh et al., ; Zellers et al., ], co-reference resolution [Sakaguchi et al., ] and general intelligence [Clark et al., ]. We include a full list of the constituent benchmarks in Table 2.2. We report the average accuracy scores across all benchmarks.
- 3.
Code. While our main focus is general performance, we also want to measure any changes to code generation performance. For code benchmarks, we focus on the function completion task. We evaluate on HumanEval-Python [Chen et al., ] and MBPP [Austin et al., ]. We report the average pass@1 scores of these benchmarks.

We evaluate performance at different scales: 470M to 2.8B parameter models. At our smaller 470M parameter size, model capabilities are limited, thus to ensure fair comparisons, we only compare benchmarks for which all models achieve performance above random. This is in line with prior work Muennighoff et al. []; Lozhkov et al. [].

LLM-as-a-judge win-rates. In addition to task-specific discriminative performance, we also evaluate generative performance using LLM-as-a-judge win-rates. The use of LLMs-as-a-Judge benchmarks [Fu et al., ; Liu et al., ; Chiang & yi Lee, ; Shimabucoro et al., ] has gained traction as an automated alternative to performing human evaluation, which tends to be laborious and expensive [Wang et al., ; Boubdir et al., ]. LLMs as evaluators compare two completions based upon detailed prompts that are a valuable proxy of the task of interest (e.g. choosing between two candidate answers, scoring based on a given attribute). Prior work has shown that using LLMs as evaluators are reasonable proxies and aligned with human preferences [Üstün et al., ; Dubois et al., ].

We use the Dolly-200 English dataset [Üstün et al., ; Singh et al., ], which consists of 200 hand-picked examples from the Dolly-15K dataset [Conover et al., ]. These prompts are open-ended and capture general-purpose non-code use cases. Hence, evaluation using this dataset is a valuable proxy for how code impacts more fluid and often open-ended asks. This is particularly valuable given the known tension between model performance on academic benchmarks and more open-ended generations – recent work has shown that as performance on open-ended generations improves, there is deterioration in traditional academic tasks [Üstün et al., ; Ouyang et al., ; Iyer et al., ; Muennighoff et al., ]. This occurs because supervised finetuning of large language models has increasingly been torn between objectives: improving traditional academic benchmarks
and training LLMs to follow instructions, acquire conversational abilities, and be helpful and harmless [Aakanksha et al., ]. Hence, to allow for a more holistic view across all performance measures, we also report win-rates.

For our win-rate evaluations, we use Cohere Command-R+(^3^33https://huggingface.co/CohereForAI/c4ai-command-r-plus) as the LLM judge. Details about the prompt are provided in Appendix B.

Table: Таблица 1: Datasets considered for evaluation: We conduct extensive evaluations across benchmarks detailed above. These provide valuable proxies for performance in natural language reasoning, world knowledge, open ended text generation, and code generation tasks.

### 2.3 Training and Model Details

For pre-trained models, we use 470M and 2.8B parameters decoder-only auto-regressive Transformer models [Radford et al., ] that are trained with a standard language modeling objective. We use parallel attention layers, [Chowdhery et al., ; Wang & Komatsuzaki, ], SwiGLU activation [Shazeer, ], no biases in dense layers, and a byte-pair-encoding (BPE) tokenizer with a vocabulary size of 256,000. All models are pre-trained using AdamW optimizer [Loshchilov & Hutter, ] with a batch size of 512 and a cosine learning rate scheduler with a warmup of 1325 steps. We use a maximum sequence length of 8192.

Infrastructure. We use TPU v5e chips(^4^44https://cloud.google.com/tpu/docs/v5e) [Jouppi et al., ] for training and evaluation. All models are trained using the FAX [Yoo et al., ] framework. In order to rigorously evaluate each ablation, we pre-train 64 models in total. This is an enormous endeavour given the scale and computational resources required. Each pre-training run for 200B tokens takes 4,736 TPU-chip hours for 470M and 13,824 TPU-chip-hours for 2.8B parameter models. Each cooldown run for 40B tokens takes 1,024 TPU-chip hours for 470M models.

## 3 Results and Discussion

In this section we will report descriptions of each experimental variant and overall results. We systematically investigate, (1) initializing an LLM with code pre-trained models (Section 3.1), and (2) the impact of model scale (Section 3.2), (3) varying proportion of code in pre-training data (Section 3.3), (4) quality and properties of the code data (Section 3.4), (5) code data in pre-training cooldown (Section 3.5). Finally, we compare the resulting pre-training recipes (Section 3.6). Figure 1 shows the key levers of our experimental design.

### 3.1 Initializing an LLM with Code Pre-trained Models

Figure: Рис. 2: Impact of initialization using code pre-trained models: Initializing model training with code pre-trained models improves reasoning and code generation compared to text-only models, where the improvement is the most when continued pre-training with high percentage text (Balanced→Text, Code→Text). Note that these variants are designed to isolate the role of initialization, so do not include cooldown.
Refer to caption: extracted/5802896/figures/init-impact-470M.png

We explore different initializations of pre-trained models to understand if using an LM with a large portion of code data as initialization improves the performance. These key ablations, along with their token counts, are summarized in Table 3.5. We briefly describe below:

- •
Text LM (text-only baseline): Pre-trained model from scratch using glorot-normal initialization [Glorot et al., ] on the text-only data for 400B tokens.
- •
Balanced LM (balanced-only): A model is trained with an equal ratio of code and text data (50% text and 50% code) in pre-training for 400B tokens.
- •
Balance-initialized Text LM (balanced → text): This model is initialized with a balanced LM (50% text and 50% code) and continually pre-trained using text data for 200B tokens.
- •
Code-initialized Text LM (code → text): Different from other variants, this model is initialized with a code-LM which is pre-trained on a code dataset for 200B tokens. The code dataset contains a mixture of 80% code data and 20% markup-style code data. We then continually pre-train this model on text for another 200B tokens.(^5^55We use a 10% of code in text mixture data during continual pre-training of code-initialized models (balanced→text, code→text) to avoid a full distribution shift and maintain the benefits of code during continual pre-training.)

Natural Language Reasoning As seen in Figure 2, initializing with 100% code pre-trained model (code→text) has the best performance for Natural language (NL) Reasoning benchmarks, and is closely followed by the balanced→text model. The code→text model and balanced→text model beat the text-only baseline on NL reasoning tasks by 8.8% and 8.2% relative improvement respectively. The balanced-only model improves upon the baseline by 3.2%. This shows that initialization from a pre-trained model with a mix of code has a strong positive effect on NL reasoning tasks. Further using a text mix with a small percentage of code for continual pre-training results in the best performance as evidenced by both the code→text and balanced→text models.

World Knowledge For World Knowledge tasks, we see that the balanced→text model has the best performance over all other variants, beating the code→text by 21% and text-only by 4.1% relative improvement. This suggests that performance on world knowledge tasks appears to depend on a more balanced data mixture for initialization and a larger proportion of text in the continual pre-training stage. Overall, code data is still beneficial compared to text-only pre-training for world knowledge tasks.

Trade-offs between NL tasks and code generation In code generation tasks, balanced-only achieves the best performance, where we see a 46.7% and 54.5% relative improvement over balanced→text and code→text. This is expected as balanced-only includes 50% code throughout pre-training. However, this model trades off better code generation with lower performance in NL tasks. code→text and balanced→text achieves 2.9% and 2.3% relative increase in NL reasoning, and 17.3% and 22.2% relative increase in world knowledge respectively compared to balanced-only.

Generative quality win-rates comparison Additionally, we compare the generative performance of each code variant (code→text and balanced-only) against the text-only model. We report win-rates and observe that the presence of code has a strong positive impact on generation quality. Both code→text and balanced-only) models beat the text-only variant by a 6.6% difference in win-loss rates. We again note that Dolly-200-English evaluation set we use for win-rate calculation is curated to reflect open ended questions and is a non-code evaluation. This confirms that code data in the pre-training mix does not only improves reasoning but also helps the model produce better quality generations. We include the extended Win-rates for these experiments in Appendix C.1.

Figure: Рис. 3: Impact of model scale on different tasks. We observe that scale provides pronounced gains across tasks of up-to 2.7x increase, however the overall trend remains the same across scales showing consistency of findings across model sizes.
Refer to caption: extracted/5802896/figures/scale-impact-1.png

### 3.2 Impact of Scale

To understand if the findings of Section 3.1 transfer to larger models, we train 2.8B parameters models with the same token budget following the same model variants at 470M scale. Figure 9 shows the results of 2.8B models in comparison with 470M results.

Comparison between 2.8B and 470M models Scaling model size to 2.8B parameters enables higher performance for all model variants in all task categories, compared to 470M results. In terms of average performance across natural language reasoning and world knowledge, balanced→text model benefits from scaling-up by a 33.1% increase relative to the same model with 470M size. The improvement for code→text and balanced-only are 31.7% and 30% relative increase.

We find that the improvements in natural language reasoning are relatively modest with 5.3%, 9.2%, and 5.2% relative gains for balanced→text, code→text, and balanced-only respectively. However, world knowledge and code performance nearly triples for all the model variants. In particular, 2.8B balanced→text results increase by 2.7x in world knowledge and 2.5x in code evaluation compared to 470M.

Trends between model variants in 2.8B Notably, in terms of initialization with code pre-trained models, the same trends seen in 470M parameter scale hold at 2.8B models. code→text and balanced→text models improve over balanced models by 6.9% and 6.1% relative gain, however, fall significantly behind in code generation performance with 43.1% and 46.3% relative drop. These results show that the trade-off between natural language tasks and code generation increases with the model size.

Overall our experiments scaling to a larger size shows that our results hold and are consistent with the trends we observe at 470M parameter ablations.

### 3.3 Code Data Proportion in Pre-training

Figure: Рис. 4: Impact of the proportion of code in pre-training for different tasks: We observe that as the code proportion of pre-training increases, the performance on code tasks increases linearly. In contrast, there is more sensitivity for NL reasoning and World knowledge tasks and an optimal range of code proportions where benefits are most tangible.
Refer to caption: extracted/5802896/figures/proportion-470M.png

In these experiments, we ablate the proportions of code data in the pre-training mixture to understand what is the optimal amount of code to maximize performance on non-code tasks. Here, we focus on the first phase of pre-training with random initialization. We train six models for 200B tokens with increasing code proportions: 0%, 25%, 50%, 75%, 90%, and 100%. The remaining proportion is filled with text data. For each variant, we train a new model independently in order to carefully ablate the impact of varying code proportions.

Natural Language Reasoning and World Knowledge For Natural Language Reasoning, as the amount of code increases, in Figure 4 we see an increase in performance compared to a text-only (0% code) model. The best performance is from a model with 25% code and 75% text, with a 3.4% relative improvement over a model with 0% code. While performance is maintained up to 75% code, it starts to rapidly erode at higher proportions with a sharp relative drop of 18.3% when the model is trained on 100% code compared to a model with no code.

For World Knowledge tasks, we see an inverse relationship with increasing the amount of code. As seen in Figure 4 middle inset, there is a slight relative drop of 3.4% at 25% code and this relative drop worsens to 31% at 75% code compared to the no-code model. The fully code model (100% code) is unable to perform in world knowledge task (86% drop relative to text-only) as there are no data sources to acquire the required knowledge in the pre-training mix.

Performance on Code For code evaluation, there is a linear increase in performance as the amount of code increases, with the best model being a code-only model. As observable in Figure 4 right inset, the 100% code leads to a 2.6x increase in the code benchmarks compared to the 25% code model. As expected, for the model with 0% code, the average pass@1 score drops to 0.

### 3.4 Influence of Code Quality and Properties on General Performance

In this section, we investigate the properties of code data by varying its quality and composition. We study this firstly (a) from the perspective of training from scratch, as we want to isolate the exact effects of different properties of code data. Secondly (b), we incorporate the best variant of the code data (high-quality synthetic code), in our continual pre-training experiments to see if the impact of the code quality transfer. We report performance on NL reasoning and Code tasks. These are the properties we study:

- •
Markup-style Data: As mentioned in Section 2.1, we separate markup-style programming languages from the rest of web-based code (Appendix A.3). We replace 20% of code-only tokens with markup-style tokens.
- •
Code Adjacent Data: Instead of using purely web-based code data, we replaced 15% percentage of code tokens with code-adjacent datasets - including GitHub issues (5%), StackExchange (5%) and Jupyter Notebooks (5%), resulting in a code-adjacent model.
- •
Code Quality: To control the quality of the code, we replaced 10% of existing code tokens with a synthetically generated high-quality code dataset. The remaining proportions of web-based code data are kept the same, resulting in a code-synth model.

Figure: (a) Code-only Pre-training
Refer to caption: extracted/5802896/figures/code-properties-base.png

Code-only pre-training We compare the above variants to a model that is trained only on web-based code data (code) from the stack dataset [Kocetkov et al., ], which forms our baseline model. Note that all these variants are pre-trained using the same amount of tokens for fair comparison (200B tokens).

In Figure 5(a), we evaluate the impact of code quality and code composition. We observe that across all variants, including diverse code sources and also synthetic code leads to gains in natural language performance relative to code, however, only synthetically generated code improves the code benchmarks. We relate this to our code evaluation where we measure performance in python, thus different programming languages or code-adjacent data slightly decrease the results. Here, code+markup and code+adjacent leads to 2.8% and 6.3% relative improvement in NL reasoning compared to code (web-code-only), but cause 15.7% and 9.4% drop in code evaluation.

Our synthetic code data (code+synth) is the most impactful ablation. It is particularly impressive given its relatively small share of the overall dataset. Despite a small weighting of 10%, the inclusion of synthetic data leads to relative improvements of 9% on natural language reasoning, and 44.9% on code benchmarks compared to the baseline of web-code-only. We note that the lifts observed for synthetic data are even more impressive given the limited amount of synthetic data available compared to code-adjacent data (3.2B tokens vs 21.4B tokens) or code+markup data (3.2B tokens vs 40B tokens), and the weighting during pre-training allocation (10% vs 15% vs 20% for synthetic data, code-adjacent, code-markup respectively). This suggests a key future lever of improvement is increasing the proportion of such high-quality code data sources.

Continual pre-training Here, based on the findings from code-only pre-training, we incorporated code+synth into our best continual pre-training variant (balanced+synth→text). We compare this against the same variant without synthetic code data (balanced→text) to evaluate the benefits of our synthetic data transfer to this setting. Again, we use the same amount of code and text tokens in these experiments.

As shown in Figure 5(b), balanced+synth→text achieves 2% and 35% relative improvement over balanced→text in NL reasoning and code, respectively. This further confirms that even a small percentage of a high-quality code data, not only improves performance in code pre-training but also increases code and non-code performance after continual pre-training with text data.

### 3.5 Code in Pre-training Cooldown

In this section, we evaluate the impact of introducing code at the final stage of pre-training. Here, we consider cooldown, where we up-weight high-quality text, math, code, and instruct-style datasets. We change the learning rate schedule from cosine-based to linear annealing with a final learning rate of $1e-6$. We evaluate the impact of including code in cooldown by comparing 3 models: a pre-trained model before cooldown, cooldown without code data, and cooldown with 20% code data. For our pre-trained model, we use balanced→text as it is our best pre-trained variant. We preserve the same token budget across variants – 40B tokens which is 10% of the token budget of the pre-trained model.

Figure: Рис. 6: Impact of code data in pre-training cooldown: Including code data in the cooldown phase improves downstream relative to cooldown with no code. Both cooldown variants improve upon no cooldown across all tasks.
Refer to caption: extracted/5802896/figures/cooldown.png

Figure: Рис. 7: Generative performance measured by win-rates for cooldown: All cooldown variants greatly benefit generative quality. We find the largest gains from cooldown with code, with the highest win-rates of 52.3 % over a model with no cooldown.
Refer to caption: extracted/5802896/figures/cooldown-winrates-ties.png

Impact of code used during cooldown in different tasks In Figure 6, we evaluate the impact of code in cooldown on model performance in NL Reasoning, world knowledge, and code benchmarks. Across tasks, we find that a cooldown with code data is most beneficial with 3.6%, 10.1%, and 20% in NL reasoning, world knowledge, and code relative to the model without cooldown.

In contrast, we find that cooldown without code does not provide any increases for both NL reasoning and Code, while providing a relative improvement of 3.1% in World Knowledge tasks compared to no cooldown, showing the critical role of code data in also cooldown phase of pre-training.

Generative win-rates after cooldown As expected, cooldown has a significant impact on generative performance as measured by win-rates (seen in Figure 7). This is because we up-weight high-quality data sources in pre-training mix including instruction-style datasets such as Dolly v2 [Conover et al., ]. Both cooldown variants (cooldown w/o code, cooldown w/ code) beat no-cooldown model by large win-rates (48.2% and 52.3%) as seen in Figure 7. Comparing the cooldown variants, including code leads an additional 4.1% generative win-rates against no-cooldown compared to cooldown without code.

Table: Таблица 2: Model variants with the corresponding pre-training recipes: Pre-training recipes include initial pre-training, continued pre-training, and cooldown phases. Balanced→Text achieves the best NL performance while Balanced-only performs significantly better in code generation.

### 3.6 Comparing Pre-Training Recipes

Considering all our experiments so far, we attempt to summarize our findings and recommend recipes for pre-training with code data. Table 3.5 shows the different variants along with pre-training phases.

Best recipe for natural language tasks As seen in Sections 3.1, 3.3, and 3.5, including code in all phases of pre-training provides improvements across all task categories. When looking at the final recipes, we find that balanced→text model followed by cooldown that includes code data corresponds to the best overall performance in natural language tasks considering NL reasoning, world knowledge, and generative performance. Notably this model achieves the highest generative win-rates with 37.7% vs 33.7 against text-only.

Best recipe for code performance Among complete recipes showing in Table 3.5, balanced-only corresponds the best performance in code benchmarks. This model achieves 20% relative gain compared to second best code→text and 55% relative gain compared to balanced→text. However, balanced-only model falls behind in natural language performance by 2.5% relative difference and 5.0% win-rate difference against text-only model both compared to balanced→text

Including code in all phases of pre-training is beneficial across our three task categories and generative performance. Our recommendation for the best overall general downstream performance would be to include a balanced mixture of code and text data during pre-training from scratch (Section 3.3), use a relatively lower code percentage during continual pre-training (Section 3.1), and include code data into cooldown mixture. Further, we recommend including high-quality code data during all phases of pre-training (Section 3.4).

Figure: Рис. 8: Generative performance as measured by win-rates for variants with full-cooldown.
Refer to caption: extracted/5802896/figures/full-cooldown-recipe-winrates-ties.png

## 4 Related work

Understanding the impact of pre-training mixes Several works have studied the effects of data age, quality, toxicity and domain of pre-training data [Longpre et al., ; Üstün et al., ]. Several works have looked at the impact of filtering [Raffel et al., ; Rae et al., ; Penedo et al., ], de-duping [Zhang et al., ] and data pruning [Lozhkov et al., ; Marion et al., ; Chimoto et al., ; Boubdir et al., ]. Furthermore, several works have considered the role of synthetic data at improving performance [Shimabucoro et al., ; Dang et al., ; Aakanksha et al., ] and helping bridge the gap in performance between open weights and proprietary models [Gunasekar et al., ; Li et al., ]. In contrast to our work which focuses explicitly on understanding the role of code, these are broader treatments that are focused on characteristics of training data as a whole.

Understanding the role of code Including code in the pre-training data mixture, even for models not specifically designed for code, has been a common practice in LLMs pre-training [Dubey et al., ; Gemini-Team et al., ; Groeneveld et al., ]. In addition to serving the popular use case in code completion and generation [Chen et al., ], previous studies suggest that the addition of code improves the performance of LLMs on various NLP tasks, such as entity linking [Kim et al., ] and commonsense reasoning [Madaan et al., ]), mathematical reasoning tasks [Liang et al., ; Madaan et al., ; Gao et al., ; Shao et al., ], and general reasoning capabilities [Muennighoff et al., ; Fu & Khot, ; Ma et al., ].

Muennighoff et al. [] demonstrated Python code data can be used to improve pretraining performance. Unlike our setting, they focused on a low-resource pre-training regime with limited data. Additionally, the evaluation set-up was limited to perplexity evaluations while the detailed breakdowns on various plausible code properties and portions were not investigated. Dubey et al. [] discuss the importance of data mixture ratio between different tokens relating to general knowledge, mathematical and reasoning, code, and multilingual. However, the distinct impact of code data was not exhaustively examined. Zhang et al. [] investigated the impact of code on LLMs’ internal reasoning capability across various tasks and model families. Different from our work, they only focus on the effect of code in the supervised fine-tuning stage (SFT), i.e. instruction fine-tuning, of the LLMs. Furthermore, they are focused solely on the impact on reasoning whereas we evaluate on a more exhaustive set of behaviors. Zhu et al. [] report the performance of their DeepSeek-Coder-V2 models on General Natural Language benchmarks. Different from our work, they compare their their chat and instruct models, and do not investigate different phases of pre-training and properties of code. To the best of our knowledge, this work is the first study that presents a thorough investigation of the impact of code in pre-training on non-code tasks. Our experiment spans several axes, with costly ablations at scale including model initialization strategies, different proportions and properties of code data, and model scales.

## 5 Conclusion

We perform a first-of-its-kind systematic study to answer ‘‘what is the impact of code data used in pre-training on a large variety of downstream tasks beyond code generation’’. We focus, not just on code performance but on downstream natural language performance, as well as generative quality using LLM-as-a-judge win-rates. We conduct ablations that look at initialization, proportions of code, quality and properties of code, and role of code in pre-training cooldown. We find across all scales of experiments that code provides critical improvements to performance on non-code tasks. Compared to text-only pre-training, for our best variant, the addition of code results in relative increase of 8.2% in natural language (NL) reasoning, 4.2% in world knowledge, 6.6% improvement in generative win-rates, and a 12x boost in code performance respectively. Further performing cooldown with code, improves 3.6%, 10.1%, and 20% in NL reasoning, world knowledge, and code relative to the model before cooldown and leads 52.3% generative win-rates.
Finally, we find that adding a small amount of high-quality synthetic data can have an outsized impact on both natural language reasoning (9% relative increase)
and code performance (44.9% relative increase).

## 6 Limitations

While we systematically study the impact of code data on downstream natural language tasks, we do not study its impact on safety. Additionally, given the nature of pre-training and the number of ablations we have conducted we were limited by the scale of larger model sizes due to prohibitive compute costs. However, given that our findings hold from 470M to 2.8B, we believe they should hold true for larger model sizes and token budgets.

## 7 Acknowledgements

We would like to thank Felipe Cruz Salinas for their help in debugging the pre-training of models. We also thank Nikolas Gristch for helping prepare the SlimPajama dataset. We thank Hangyu (John) Lin and David Cairuz for their guidance in preparing datasets and training models. We thank Daniel D’souza for their help in plotting.

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Pitman, Rushin Shah, Emanuel Taropa, Majd Al Merey, Martin Baeuml, Zhifeng Chen, Laurent El Shafey, Yujing Zhang, Olcan Sercinoglu, George Tucker, Enrique Piqueras, Maxim Krikun, Iain Barr, Nikolay Savinov, Ivo Danihelka, Becca Roelofs, Anaïs White, Anders Andreassen, Tamara von Glehn, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, Alexandre Frechette, Charlotte Smith, Laura Culp, Lev Proleev, Yi Luan, Xi Chen, James Lottes, Nathan Schucher, Federico Lebron, Alban Rrustemi, Natalie Clay, Phil Crone, Tomas Kocisky, Jeffrey Zhao, Bartek Perz, Dian Yu, Heidi Howard, Adam Bloniarz, Jack W. Rae, Han Lu, Laurent Sifre, Marcello Maggioni, Fred Alcober, Dan Garrette, Megan Barnes, Shantanu Thakoor, Jacob Austin, Gabriel Barth-Maron, William Wong, Rishabh Joshi, Rahma Chaabouni, Deeni Fatiha, Arun Ahuja, Gaurav Singh Tomar, Evan Senter, Martin Chadwick, Ilya Kornakov, Nithya Attaluri, Iñaki Iturrate, Ruibo Liu, Yunxuan Li, Sarah Cogan, Jeremy Chen, Chao Jia, Chenjie Gu, Qiao Zhang,
Jordan Grimstad, Ale Jakse Hartman, Xavier Garcia, Thanumalayan Sankaranarayana Pillai, Jacob Devlin, Michael Laskin, Diego de Las Casas, Dasha Valter, Connie Tao, Lorenzo Blanco, Adrià Puigdomènech Badia, David Reitter, Mianna Chen, Jenny Brennan, Clara Rivera, Sergey Brin, Shariq Iqbal, Gabriela Surita, Jane Labanowski, Abhi Rao, Stephanie Winkler, Emilio Parisotto, Yiming Gu, Kate Olszewska, Ravi Addanki, Antoine Miech, Annie Louis, Denis Teplyashin, Geoff Brown, Elliot Catt, Jan Balaguer, Jackie Xiang, Pidong Wang, Zoe Ashwood, Anton Briukhov, Albert Webson, Sanjay Ganapathy, Smit Sanghavi, Ajay Kannan, Ming-Wei Chang, Axel Stjerngren, Josip Djolonga, Yuting Sun, Ankur Bapna, Matthew Aitchison, Pedram Pejman, Henryk Michalewski, Tianhe Yu, Cindy Wang, Juliette Love, Junwhan Ahn, Dawn Bloxwich, Kehang Han, Peter Humphreys, Thibault Sellam, James Bradbury, Varun Godbole, Sina Samangooei, Bogdan Damoc, Alex Kaskasoli, Sébastien M. R. Arnold, Vijay Vasudevan, Shubham Agrawal, Jason Riesa, Dmitry
Lepikhin, Richard Tanburn, Srivatsan Srinivasan, Hyeontaek Lim, Sarah Hodkinson, Pranav Shyam, Johan Ferret, Steven Hand, Ankush Garg, Tom Le Paine, Jian Li, Yujia Li, Minh Giang, Alexander Neitz, Zaheer Abbas, Sarah York, Machel Reid, Elizabeth Cole, Aakanksha Chowdhery, Dipanjan Das, Dominika Rogozińska, Vitaliy Nikolaev, Pablo Sprechmann, Zachary Nado, Lukas Zilka, Flavien Prost, Luheng He, Marianne Monteiro, Gaurav Mishra, Chris Welty, Josh Newlan, Dawei Jia, Miltiadis Allamanis, Clara Huiyi Hu, Raoul de Liedekerke, Justin Gilmer, Carl Saroufim, Shruti Rijhwani, Shaobo Hou, Disha Shrivastava, Anirudh Baddepudi, Alex Goldin, Adnan Ozturel, Albin Cassirer, Yunhan Xu, Daniel Sohn, Devendra Sachan, Reinald Kim Amplayo, Craig Swanson, Dessie Petrova, Shashi Narayan, Arthur Guez, Siddhartha Brahma, Jessica Landon, Miteyan Patel, Ruizhe Zhao, Kevin Villela, Luyu Wang, Wenhao Jia, Matthew Rahtz, Mai Giménez, Legg Yeung, James Keeling, Petko Georgiev, Diana Mincu, Boxi Wu, Salem Haykal, Rachel Saputro, Kiran
Vodrahalli, James Qin, Zeynep Cankara, Abhanshu Sharma, Nick Fernando, Will Hawkins, Behnam Neyshabur, Solomon Kim, Adrian Hutter, Priyanka Agrawal, Alex Castro-Ros, George van den Driessche, Tao Wang, Fan Yang, Shuo yiin Chang, Paul Komarek, Ross McIlroy, Mario Lučić, Guodong Zhang, Wael Farhan, Michael Sharman, Paul Natsev, Paul Michel, Yamini Bansal, Siyuan Qiao, Kris Cao, Siamak Shakeri, Christina Butterfield, Justin Chung, Paul Kishan Rubenstein, Shivani Agrawal, Arthur Mensch, Kedar Soparkar, Karel Lenc, Timothy Chung, Aedan Pope, Loren Maggiore, Jackie Kay, Priya Jhakra, Shibo Wang, Joshua Maynez, Mary Phuong, Taylor Tobin, Andrea Tacchetti, Maja Trebacz, Kevin Robinson, Yash Katariya, Sebastian Riedel, Paige Bailey, Kefan Xiao, Nimesh Ghelani, Lora Aroyo, Ambrose Slone, Neil Houlsby, Xuehan Xiong, Zhen Yang, Elena Gribovskaya, Jonas Adler, Mateo Wirth, Lisa Lee, Music Li, Thais Kagohara, Jay Pavagadhi, Sophie Bridgers, Anna Bortsova, Sanjay Ghemawat, Zafarali Ahmed, Tianqi Liu, Richard Powell,
Vijay Bolina, Mariko Iinuma, Polina Zablotskaia, James Besley, Da-Woon Chung, Timothy Dozat, Ramona Comanescu, Xiance Si, Jeremy Greer, Guolong Su, Martin Polacek, Raphaël Lopez Kaufman, Simon Tokumine, Hexiang Hu, Elena Buchatskaya, Yingjie Miao, Mohamed Elhawaty, Aditya Siddhant, Nenad Tomasev, Jinwei Xing, Christina Greer, Helen Miller, Shereen Ashraf, Aurko Roy, Zizhao Zhang, Ada Ma, Angelos Filos, Milos Besta, Rory Blevins, Ted Klimenko, Chih-Kuan Yeh, Soravit Changpinyo, Jiaqi Mu, Oscar Chang, Mantas Pajarskas, Carrie Muir, Vered Cohen, Charline Le Lan, Krishna Haridasan, Amit Marathe, Steven Hansen, Sholto Douglas, Rajkumar Samuel, Mingqiu Wang, Sophia Austin, Chang Lan, Jiepu Jiang, Justin Chiu, Jaime Alonso Lorenzo, Lars Lowe Sjösund, Sébastien Cevey, Zach Gleicher, Thi Avrahami, Anudhyan Boral, Hansa Srinivasan, Vittorio Selo, Rhys May, Konstantinos Aisopos, Léonard Hussenot, Livio Baldini Soares, Kate Baumli, Michael B. Chang, Adrià Recasens, Ben Caine, Alexander Pritzel, Filip Pavetic,
Fabio Pardo, Anita Gergely, Justin Frye, Vinay Ramasesh, Dan Horgan, Kartikeya Badola, Nora Kassner, Subhrajit Roy, Ethan Dyer, Víctor Campos Campos, Alex Tomala, Yunhao Tang, Dalia El Badawy, Elspeth White, Basil Mustafa, Oran Lang, Abhishek Jindal, Sharad Vikram, Zhitao Gong, Sergi Caelles, Ross Hemsley, Gregory Thornton, Fangxiaoyu Feng, Wojciech Stokowiec, Ce Zheng, Phoebe Thacker, Çağlar Ünlü, Zhishuai Zhang, Mohammad Saleh, James Svensson, Max Bileschi, Piyush Patil, Ankesh Anand, Roman Ring, Katerina Tsihlas, Arpi Vezer, Marco Selvi, Toby Shevlane, Mikel Rodriguez, Tom Kwiatkowski, Samira Daruki, Keran Rong, Allan Dafoe, Nicholas FitzGerald, Keren Gu-Lemberg, Mina Khan, Lisa Anne Hendricks, Marie Pellat, Vladimir Feinberg, James Cobon-Kerr, Tara Sainath, Maribeth Rauh, Sayed Hadi Hashemi, Richard Ives, Yana Hasson, Eric Noland, Yuan Cao, Nathan Byrd, Le Hou, Qingze Wang, Thibault Sottiaux, Michela Paganini, Jean-Baptiste Lespiau, Alexandre Moufarek, Samer Hassan, Kaushik Shivakumar, Joost van
Amersfoort, Amol Mandhane, Pratik Joshi, Anirudh Goyal, Matthew Tung, Andrew Brock, Hannah Sheahan, Vedant Misra, Cheng Li, Nemanja Rakićević, Mostafa Dehghani, Fangyu Liu, Sid Mittal, Junhyuk Oh, Seb Noury, Eren Sezener, Fantine Huot, Matthew Lamm, Nicola De Cao, Charlie Chen, Sidharth Mudgal, Romina Stella, Kevin Brooks, Gautam Vasudevan, Chenxi Liu, Mainak Chain, Nivedita Melinkeri, Aaron Cohen, Venus Wang, Kristie Seymore, Sergey Zubkov, Rahul Goel, Summer Yue, Sai Krishnakumaran, Brian Albert, Nate Hurley, Motoki Sano, Anhad Mohananey, Jonah Joughin, Egor Filonov, Tomasz Kępa, Yomna Eldawy, Jiawern Lim, Rahul Rishi, Shirin Badiezadegan, Taylor Bos, Jerry Chang, Sanil Jain, Sri Gayatri Sundara Padmanabhan, Subha Puttagunta, Kalpesh Krishna, Leslie Baker, Norbert Kalb, Vamsi Bedapudi, Adam Kurzrok, Shuntong Lei, Anthony Yu, Oren Litvin, Xiang Zhou, Zhichun Wu, Sam Sobell, Andrea Siciliano, Alan Papir, Robby Neale, Jonas Bragagnolo, Tej Toor, Tina Chen, Valentin Anklin, Feiran Wang, Richie Feng, Milad
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Hidey, Niharika Ahuja, Pranab Saxena, Dan Dooley, Srividya Pranavi Potharaju, Eileen O’Neill, Anand Gokulchandran, Ryan Foley, Kai Zhao, Mike Dusenberry, Yuan Liu, Pulkit Mehta, Ragha Kotikalapudi, Chalence Safranek-Shrader, Andrew Goodman, Joshua Kessinger, Eran Globen, Prateek Kolhar, Chris Gorgolewski, Ali Ibrahim, Yang Song, Ali Eichenbaum, Thomas Brovelli, Sahitya Potluri, Preethi Lahoti, Cip Baetu, Ali Ghorbani, Charles Chen, Andy Crawford, Shalini Pal, Mukund Sridhar, Petru Gurita, Asier Mujika, Igor Petrovski, Pierre-Louis Cedoz, Chenmei Li, Shiyuan Chen, Niccolò Dal Santo, Siddharth Goyal, Jitesh Punjabi, Karthik Kappaganthu, Chester Kwak, Pallavi LV, Sarmishta Velury, Himadri Choudhury, Jamie Hall, Premal Shah, Ricardo Figueira, Matt Thomas, Minjie Lu, Ting Zhou, Chintu Kumar, Thomas Jurdi, Sharat Chikkerur, Yenai Ma, Adams Yu, Soo Kwak, Victor Ähdel, Sujeevan Rajayogam, Travis Choma, Fei Liu, Aditya Barua, Colin Ji, Ji Ho Park, Vincent Hellendoorn, Alex Bailey, Taylan Bilal, Huanjie Zhou,
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Ben-David, Rishub Jain, Jonathan Uesato, Romina Datta, Oskar Bunyan, Shimu Wu, John Zhang, Piotr Stanczyk, Ye Zhang, David Steiner, Subhajit Naskar, Michael Azzam, Matthew Johnson, Adam Paszke, Chung-Cheng Chiu, Jaume Sanchez Elias, Afroz Mohiuddin, Faizan Muhammad, Jin Miao, Andrew Lee, Nino Vieillard, Jane Park, Jiageng Zhang, Jeff Stanway, Drew Garmon, Abhijit Karmarkar, Zhe Dong, Jong Lee, Aviral Kumar, Luowei Zhou, Jonathan Evens, William Isaac, Geoffrey Irving, Edward Loper, Michael Fink, Isha Arkatkar, Nanxin Chen, Izhak Shafran, Ivan Petrychenko, Zhe Chen, Johnson Jia, Anselm Levskaya, Zhenkai Zhu, Peter Grabowski, Yu Mao, Alberto Magni, Kaisheng Yao, Javier Snaider, Norman Casagrande, Evan Palmer, Paul Suganthan, Alfonso Castaño, Irene Giannoumis, Wooyeol Kim, Mikołaj Rybiński, Ashwin Sreevatsa, Jennifer Prendki, David Soergel, Adrian Goedeckemeyer, Willi Gierke, Mohsen Jafari, Meenu Gaba, Jeremy Wiesner, Diana Gage Wright, Yawen Wei, Harsha Vashisht, Yana Kulizhskaya, Jay Hoover, Maigo Le,
Lu Li, Chimezie Iwuanyanwu, Lu Liu, Kevin Ramirez, Andrey Khorlin, Albert Cui, Tian LIN, Marcus Wu, Ricardo Aguilar, Keith Pallo, Abhishek Chakladar, Ginger Perng, Elena Allica Abellan, Mingyang Zhang, Ishita Dasgupta, Nate Kushman, Ivo Penchev, Alena Repina, Xihui Wu, Tom van der Weide, Priya Ponnapalli, Caroline Kaplan, Jiri Simsa, Shuangfeng Li, Olivier Dousse, Fan Yang, Jeff Piper, Nathan Ie, Rama Pasumarthi, Nathan Lintz, Anitha Vijayakumar, Daniel Andor, Pedro Valenzuela, Minnie Lui, Cosmin Paduraru, Daiyi Peng, Katherine Lee, Shuyuan Zhang, Somer Greene, Duc Dung Nguyen, Paula Kurylowicz, Cassidy Hardin, Lucas Dixon, Lili Janzer, Kiam Choo, Ziqiang Feng, Biao Zhang, Achintya Singhal, Dayou Du, Dan McKinnon, Natasha Antropova, Tolga Bolukbasi, Orgad Keller, David Reid, Daniel Finchelstein, Maria Abi Raad, Remi Crocker, Peter Hawkins, Robert Dadashi, Colin Gaffney, Ken Franko, Anna Bulanova, Rémi Leblond, Shirley Chung, Harry Askham, Luis C. Cobo, Kelvin Xu, Felix Fischer, Jun Xu, Christina Sorokin,
Chris Alberti, Chu-Cheng Lin, Colin Evans, Alek Dimitriev, Hannah Forbes, Dylan Banarse, Zora Tung, Mark Omernick, Colton Bishop, Rachel Sterneck, Rohan Jain, Jiawei Xia, Ehsan Amid, Francesco Piccinno, Xingyu Wang, Praseem Banzal, Daniel J. Mankowitz, Alex Polozov, Victoria Krakovna, Sasha Brown, MohammadHossein Bateni, Dennis Duan, Vlad Firoiu, Meghana Thotakuri, Tom Natan, Matthieu Geist, Ser tan Girgin, Hui Li, Jiayu Ye, Ofir Roval, Reiko Tojo, Michael Kwong, James Lee-Thorp, Christopher Yew, Danila Sinopalnikov, Sabela Ramos, John Mellor, Abhishek Sharma, Kathy Wu, David Miller, Nicolas Sonnerat, Denis Vnukov, Rory Greig, Jennifer Beattie, Emily Caveness, Libin Bai, Julian Eisenschlos, Alex Korchemniy, Tomy Tsai, Mimi Jasarevic, Weize Kong, Phuong Dao, Zeyu Zheng, Frederick Liu, Fan Yang, Rui Zhu, Tian Huey Teh, Jason Sanmiya, Evgeny Gladchenko, Nejc Trdin, Daniel Toyama, Evan Rosen, Sasan Tavakkol, Linting Xue, Chen Elkind, Oliver Woodman, John Carpenter, George Papamakarios, Rupert Kemp, Sushant
Kafle, Tanya Grunina, Rishika Sinha, Alice Talbert, Diane Wu, Denese Owusu-Afriyie, Cosmo Du, Chloe Thornton, Jordi Pont-Tuset, Pradyumna Narayana, Jing Li, Saaber Fatehi, John Wieting, Omar Ajmeri, Benigno Uria, Yeongil Ko, Laura Knight, Amélie Héliou, Ning Niu, Shane Gu, Chenxi Pang, Yeqing Li, Nir Levine, Ariel Stolovich, Rebeca Santamaria-Fernandez, Sonam Goenka, Wenny Yustalim, Robin Strudel, Ali Elqursh, Charlie Deck, Hyo Lee, Zonglin Li, Kyle Levin, Raphael Hoffmann, Dan Holtmann-Rice, Olivier Bachem, Sho Arora, Christy Koh, Soheil Hassas Yeganeh, Siim Põder, Mukarram Tariq, Yanhua Sun, Lucian Ionita, Mojtaba Seyedhosseini, Pouya Tafti, Zhiyu Liu, Anmol Gulati, Jasmine Liu, Xinyu Ye, Bart Chrzaszcz, Lily Wang, Nikhil Sethi, Tianrun Li, Ben Brown, Shreya Singh, Wei Fan, Aaron Parisi, Joe Stanton, Vinod Koverkathu, Christopher A. Choquette-Choo, Yunjie Li, TJ Lu, Abe Ittycheriah, Prakash Shroff, Mani Varadarajan, Sanaz Bahargam, Rob Willoughby, David Gaddy, Guillaume Desjardins, Marco Cornero, Brona
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Martin Wicke, Xiao Ma, Evgenii Eltyshev, Nina Martin, Hardie Cate, James Manyika, Keyvan Amiri, Yelin Kim, Xi Xiong, Kai Kang, Florian Luisier, Nilesh Tripuraneni, David Madras, Mandy Guo, Austin Waters, Oliver Wang, Joshua Ainslie, Jason Baldridge, Han Zhang, Garima Pruthi, Jakob Bauer, Feng Yang, Riham Mansour, Jason Gelman, Yang Xu, George Polovets, Ji Liu, Honglong Cai, Warren Chen, XiangHai Sheng, Emily Xue, Sherjil Ozair, Christof Angermueller, Xiaowei Li, Anoop Sinha, Weiren Wang, Julia Wiesinger, Emmanouil Koukoumidis, Yuan Tian, Anand Iyer, Madhu Gurumurthy, Mark Goldenson, Parashar Shah, MK Blake, Hongkun Yu, Anthony Urbanowicz, Jennimaria Palomaki, Chrisantha Fernando, Ken Durden, Harsh Mehta, Nikola Momchev, Elahe Rahimtoroghi, Maria Georgaki, Amit Raul, Sebastian Ruder, Morgan Redshaw, Jinhyuk Lee, Denny Zhou, Komal Jalan, Dinghua Li, Blake Hechtman, Parker Schuh, Milad Nasr, Kieran Milan, Vladimir Mikulik, Juliana Franco, Tim Green, Nam Nguyen, Joe Kelley, Aroma Mahendru, Andrea Hu, Joshua
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## Приложение A Code-datasets filtering

### A.1 Quality filters

In addition to the deduplication and quality filtering applied on the GitHub scrapes by Starcoder for The Stack dataset [Li et al., ], we apply filters to remove documents with greater than 1000 float numbers, with instances of the string 0x, that are lists of top-level domains, and with ’generated by’ in the first 400 characters

### A.2 Programming languages present in Web-based Code dataset

Table: Таблица 3: Programming languages included in our version of The Stack dataset

### A.3 Markup-style programming languages present in Web-based Code dataset

Table: Таблица 4: Markup-style languages included in our version of The Stack dataset

## Приложение B LLM judge Prompt and Preamble for win-rates

Preamble

You are a helpful following assistant whose goal is to select the preferred
(least wrong) output for a given instruction.

Prompt

Which of the following answers is the best one for the given instruction.
A good answer should follow these rules:
1) It should have correct reasoning,
2) It should answer the request in the instruction,
3) It should be factually correct and semantically comprehensible,
4) It should be grammatically correct and fluent.
Instruction: instruction
Answer (A): completion_a
Answer (B): completion_b
FIRST provide a concise comparison of the two answers which explains
which answer you prefer and why.
SECOND, on a new line, state exactly one of
‘Preferred: Answer (A)’ or ‘Preferred: Answer (B)’ to indicate your choice
of preferred response.
Your response should use the format:
Comparison: <concise comparison and explanation>
Preferred: <‘Answer (A)’ or ‘Answer (B)’>

## Приложение C Results

### C.1 Generative win-rates for impact of initialization

Figure: Рис. 9: Impact of initialization on generative quality as judged by LLM-as-a-judge.
Refer to caption: extracted/5802896/figures/init-impact-470M-winrates-ties.png

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: - In the sentence "Figure 8 shows the results of xxx" in Section 3.2, it seems that "Figure 8" should actually be "Figure 3." May be a typo?
soundness: 3
strengths: - The paper addresses a crucial question: does code data improve performance on non-code tasks? This question has been relatively unexplored, and the paper offers a fresh perspective on the potential benefits of code data for models primarily intended for natural language tasks.

- The methodology is rigorous, employing controlled experiments that examine various factors, such as code quality, proportions, and stages of pre-training. The large-scale experiments (up to 2.8B parameters) provide an in-depth understanding of how code data impacts model performance.

- This work has significant implications for pre-training dataset design, suggesting that code data improves generalization and that high-quality synthetic code can be particularly impactful. This insight can guide future LLM development, especially in choosing and refining pre-training data.
summary: The paper investigates the impact of incorporating code data into pre-training for large language models (LLMs).
It explores how code data influences various tasks, including natural language reasoning, world knowledge, and open-ended generation. 
Through extensive ablation studies, the authors reveal that adding code data yields substantial benefits across tasks beyond coding. 
The study emphasizes code quality and finds that synthetic, high-quality code data further enhances general performance. 
The results imply that code data should be included in the pre-training process as it significantly enhances generalization across tasks.
weaknesses: - For the scaling experiments, some key information could be explored further. For instance, does the trend observed in Figure 4 hold consistently across models of different scales? Additionally, how can the trends identified be extended or generalized to larger models?

- I am also curious about the model's performance on mathematical tasks, as these, like coding, are highly logical and reasoning-intensive. Including a benchmark evaluation on mathematical tasks could provide valuable insight into whether similar gains extend to other logic-focused domains.

- In terms of writing, certain critical details could be clarified. For instance, it may not be immediately clear in the text which model size (e.g., 470M or 2.8B) is being evaluated in figures such as Figures 4 and 5. Adding such context in the captions or main text would enhance clarity for readers.

**Given the close relationship between scaling laws and research on dataset composition, there is still room to refine and expand upon some of the paper's exploratory findings.**

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: The investigation into the role of code in LLM pre-training addresses a crucial aspect of model performance with good implications for both research and practical applications. The exploration of the impact of code data and strategies for leveraging it provides valuable insights into enhancing model effectiveness, contributing to a deeper understanding of LLM capabilities.

I have two concerns:

1. Limited Technical Contribution: The study primarily consists of empirical analyses without introducing new methodologies or frameworks. While the findings are good, the technical contribution could be perceived as limited due to the absence of novel approaches or theoretical advancements.

2. Limited Scale: The analysis is restricted to models with 470M and 2.8B parameters, which may not capture the performance dynamics across a broader range of model sizes and architectures.
soundness: 3
strengths: + Important Area.
+ Interesting Finding.
+ Analysis of the Code Quality is Good.
summary: The authors examine the role of code data in the pre-training of large language models (LLMs), a practice that has become increasingly common, even for models not specifically designed for code. Despite anecdotal consensus among practitioners regarding the importance of code in enhancing the performance of general LLMs, there has been limited research analyzing its precise impact on non-code tasks. This study systematically investigates the influence of code data in pre-training on a diverse array of downstream tasks beyond code generation. The authors pose the question: “What is the impact of code data used in pre-training on a large variety of downstream tasks?” Through extensive ablation studies, they evaluate models ranging from 470M to 2.8B parameters across natural language reasoning tasks, world knowledge tasks, code benchmarks, and LLM-as-a-judge win rates. The results consistently demonstrate that code serves as a critical building block for generalization well beyond coding tasks. Improvements in code quality significantly enhance overall task performance, with findings indicating up to an 8.2% relative increase in natural language reasoning, a 4.2% boost in world knowledge, a 6.6% improvement in generative win rates, and a 12x enhancement in code performance when compared to text-only pre-training. The authors suggest that investing in code quality and retaining code during pre-training can yield substantial benefits across various tasks.
weaknesses: - Technical Contribution is Weak.
- Limited Scale.

---

confidence: 4
contribution: 1
initial_rating: 3
presentation: 2
questions: 1. Given the observed performance drop on certain NL tasks, how does the study reconcile this finding with the broader claim of code data’s benefits for NLP tasks?

2. Have the authors considered evaluating the pre-trained models on additional natural language tasks to better understand the generalization effects of code data?
soundness: 1
strengths: • The paper provides insights into the effects of code data on pre-training across a range of NLP tasks.

• Experiments are conducted with multiple configurations, such as balancing code and text data, adding depth to the analysis.
summary: This paper investigates the impact of code data in pre-training for NLP tasks. The authors pre-train Transformer models with sizes ranging from 470M to 2.8B parameters with ablations of code and non-code data. The pre-trained models are evaluated on downstream tasks such as NL reasoning, world knowledge, and code benchmarks. Experimental results show that incorporating code into pre-training has a positive impact on non-coding tasks. In addition, improvements to code quality have an outsized impact across all tasks. In particular, compared to text-only pre-training, the addition of code results in up to relative increase of 8.2% in natural language (NL) reasoning, 4.2% in world knowledge, 6.6% improvement in generative win-rates, and a 12x boost in code performance respectively. The results suggest future investments in code quality and preserving code during pre-training.
weaknesses: • The findings are somewhat unsurprising, as incorporating diverse data sources (like code) in pre-training is known to improve model performance—a principle demonstrated by previous studies, including PaLM (Chowdhery et al., 2022), Gopher (Rae et al., 2022), and Bloom (Workshop et al., 2023). This paper extends these findings by analyzing code quality and proportion but does not significantly depart from established conclusions, such as the importance of data quality in pre-training (https://arxiv.org/abs/2207.05579).

• Figure 4 suggests that including code data may actually reduce performance on some natural language tasks, which appears contradictory to the paper’s main claim. This inconsistency weakens the overall reliability of the conclusions.

• The pre-training models used in this study are relatively small (470M and 2.8B parameters), which limits the generalizability of the findings. Larger models may be necessary to substantiate conclusions regarding the effects of code data on NLP pre-training.

---

confidence: 4
contribution: 3
initial_rating: 8
presentation: 3
questions: - Could the authors clarify whether the Balanced LM (50% text and 50% code) incorporates markup-style data in its training mix? The paper specifies that the Code-initialized model includes 80% code data and 20% markup-style data, but it remains unclear whether this type of data is also included in the Balanced LM.
soundness: 3
strengths: - _Novelty_: This work is a first-of-its-kind, comprehensive study on how code data in pre-training affects LLMs across various natural language and coding tasks, offering new insights into data requirements for LLM pre-training.

- _Comprehensiveness_: The authors carry out a broad set of experiments, exploring various scales of model size, data quantity, and training stages. Their work provides actionable guidance for future LLM development.
summary: ## Summary:
The authors conduct a systematic investigation into the role of code data in the pre-training phase of LLMs, assessing the impact on three task categories: natural language reasoning, world knowledge, and code generation. They analyze three pre-training stages, including initial pre-training, continual pre-training, and a cooldown phase; evaluate the effect of code data’s proportion, quality, and properties on the model’s performance across the three task categories. Their results demonstrate that incorporating code data enhances model performance across all the pre-training stages, suggesting a balanced approach with optimized code data quality can boost the model’s ability in reasoning, general knowledge, and coding.
weaknesses: 1. **Limited Details on High-Quality Code Data**: A significant portion of the paper’s conclusions, particularly in Section 3.4, emphasize the positive impact of incorporating high-quality code data on LLM performance across different task categories. However, the description of the high-quality code data is limited to that it is proprietary and synthetically generated. The lack of transparency about the dataset's characteristics, such as the source, data format, or complexity raises questions about what specifically constitutes "high quality" and how these qualities directly influence performance. More details on criteria for data quality would strengthen the paper and enable the research community to apply similar quality standards in future work.

2. **Potential Bias in Generative Ability Evaluation**: The evaluation of generative ability relies on the LLM-as-a-Judge approach, specifically through win-rate scores on the Dolly-200-English dataset, which is designed to reflect open-ended, non-code tasks. The authors attribute improvements in generative quality to the inclusion of code data in pre-training, arguing that code data enriches the model’s ability to generate preferred outputs. However, using LLMs to evaluate other LLM outputs has known limitations[1], including potential biases toward verbosity, structured formatting, and positional advantage. Moreover, the inclusion of markup-style code may enhance readability and formatting, making responses more appealing to the LLM evaluator. To mitigate ambiguity, qualitative examples of output differences would be helpful in illustrating how the inclusion of code data contributes to generative improvements beyond format and readability alone.

3. **Minor Issues**: 
   - In Section 3.2, Figure 3 is referenced incorrectly as Figure 8, which could confuse readers. 
   - The legend for Figure 8 lacks a label for the gray bar, which reduces clarity in interpreting the data presented.

[1] Zheng, L., Chiang, W. L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., ... & Stoica, I. (2023). Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems, 36, 46595-46623.

Meta-review: This paper investigates an important question of whether code data can be used to improve performance on non-code tasks and has been well-received by all reviewers. The reviewers unanimously agree that the results are impressive, demonstrating significant contributions to the field. Congratulations to the authors on the acceptance of this paper.

Decision: Accept (Poster)