* minimal implementation, simplified challenge registry
* need game save logic
* fixed challenge gen, works with local test
* updated challenge gen with wider ranges, working with local script
* runs working correctly, wandb stats look ok
* linting
* Add diplomacy environment with AI_Diplomacy submodule
- Add diplomacy_env_minimal.py for diplomacy game environment
- Add atropos_client_minimal.py for client interface
- Add diplomacy_local_server.py for local game server
- Add AI_Diplomacy submodule from GoodStartLabs/AI_Diplomacy
- Fix import ordering and remove unused imports
* test file working, moving to cluster to test training
* updated gitignore
* removed logs
* minor fixes, training running now
* readded proxy reg and queue system
* linting
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* queue gameid bug, refactored
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* cleaned up configs & allowed for openrouter models to be easily used
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* linting
* Remove duplicate dependencies from diplomacy requirements.txt
Only keep AI_Diplomacy-specific dependencies that aren't already in the main project
---------
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>
- Made reward field truly optional in messages (no auto-addition)
- Accept custom roles (dog, cat, etc.) beyond standard ones
- Added 24 new tests for edge cases (tuples, unicode, large content)
- Reorganized test structure: moved from testing/ to atroposlib/tests/
- Fixed legacy API tests and removed tests requiring missing data files
All 43 tests pass\! Fixes message handling for SFT use cases.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
