Automatically save the final merged evaluate configuration to evaluate_config.yaml
in the data_dir_to_save_evals directory. This includes env config, OpenAI/server
configs, and server manager settings, enabling reproducibility and easier
debugging of evaluation runs.
The config is saved after all merging (CLI args > YAML > defaults) to capture
the exact configuration used for the evaluation.
* removed changes to other files
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fail on scores empty
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
- Add min_batch_allocation parameter to ensure environments contribute minimum proportion to each batch
- Implement grab_batch_with_minimum_allocations function with proper scaling when allocations exceed 100%
- Add mixed-size group buffering to handle variable-sized data submissions
- Update server to use minimum allocation logic when any env has min_batch_allocation set
- Add comprehensive tests for minimum allocation scenarios
- Update documentation in API README and CONFIG.md
- Update example environments to demonstrate the feature
This feature allows critical environments to guarantee they contribute at least a specified proportion (0.0-1.0) to each training batch, ensuring important data sources are always represented during training.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Added optional fields: advantages, messages, and images to the ScoredData model.
- Updated API responses to include these new fields when no data is available.
- Revised README.md to reflect changes in the API structure and response format.
