Datasets:

nebius
/

SWE-bench-extra

@@ -49,7 +49,7 @@ dataset_info:
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 # Dataset Summary
-SWE-bench Extra is a dataset that can be used to train or evaluate agentic systems specializing in resolving GitHub issues. It is based on the methodology used to build SWE-bench benchmark and includes 6,448 Issue-Pull Request pairs sourced from 2,129 Python repositories.
 # Dataset Description
 The SWE-bench Extra dataset supports the development of software engineering agents capable of autonomously solving GitHub issues. The data collection process, based on the SWE-bench methodology, involves the following steps:
@@ -58,7 +58,7 @@ The SWE-bench Extra dataset supports the development of software engineering age
 2. **Filtering**: Instances are filtered based on attributes such as issue descriptions, relevant code paths, and test patches.
 3. **Execution-based Validation**: The project environments are set up and tests are run to verify that they execute correctly.
-For a more detailed description of the data collection process, please refer to our blog post. [link]
 As an example use case of this dataset, we’ve used SWE-bench-extra instances to generate a dataset of 80,036 trajectories [`nebius/swe-agent-trajectories`](https://huggingface.co/datasets/nebius/swe-agent-trajectories). We’ve then trained an action generator model, that achieves a score of 19.2% on the subset of 50 random instances from the SWE-bench Verified benchmark, representing a 30% relative improvement over its parent model Qwen2.5-72B-Instruct, which scored 14.8%. Further augmenting the action generator with a guided search based on a critic model, also trained on this data, achieves 40.6% on the full SWE-bench Verified benchmark, which is state-of-the-art among agents using solely open-weight models. You can read more about this agent in our blog post, [“Leveraging Training and Search for Better Software Engineering Agents”](https://nebius.com/blog/posts/training-and-search-for-software-engineering-agents).

 ---
 # Dataset Summary
+SWE-bench Extra is a dataset that can be used to train or evaluate agentic systems specializing in resolving GitHub issues. It is based on the methodology used to build SWE-bench benchmark and includes 6,415 Issue-Pull Request pairs sourced from 1,988 Python repositories.
 # Dataset Description
 The SWE-bench Extra dataset supports the development of software engineering agents capable of autonomously solving GitHub issues. The data collection process, based on the SWE-bench methodology, involves the following steps:
 2. **Filtering**: Instances are filtered based on attributes such as issue descriptions, relevant code paths, and test patches.
 3. **Execution-based Validation**: The project environments are set up and tests are run to verify that they execute correctly.
+For a more detailed description of the data collection process, please refer to our blog post [Scaling data collection for training software engineering agents](https://nebius.com/blog/posts/scaling-data-collection-for-training-swe-agents).
 As an example use case of this dataset, we’ve used SWE-bench-extra instances to generate a dataset of 80,036 trajectories [`nebius/swe-agent-trajectories`](https://huggingface.co/datasets/nebius/swe-agent-trajectories). We’ve then trained an action generator model, that achieves a score of 19.2% on the subset of 50 random instances from the SWE-bench Verified benchmark, representing a 30% relative improvement over its parent model Qwen2.5-72B-Instruct, which scored 14.8%. Further augmenting the action generator with a guided search based on a critic model, also trained on this data, achieves 40.6% on the full SWE-bench Verified benchmark, which is state-of-the-art among agents using solely open-weight models. You can read more about this agent in our blog post, [“Leveraging Training and Search for Better Software Engineering Agents”](https://nebius.com/blog/posts/training-and-search-for-software-engineering-agents).