update to tech report version (#10)

* feat(run_eval): add checkpoint resume functionality and update example documentation;
- update new bootcamp benchmark dataset

* refactor(data_pipeline): optimize data generation pipeline; add multiple preset configurations for data generation

* docs: update bootcamp list and add new scripts

- Update Fulllist_InternBootcamp.md with new bootcamps and categories
- Add new scripts to .gitignore:
  - examples/pipelines/filter_autogen_configs.py
  - examples/pipelines/quickgen_data_configs_from_eval_meta.py
- Update dependencies in setup.py:
  - Add scipy and scikit-learn

* refactor(internbootcamp): update bootcamp modules and improve error handling

- Update import statements in __init__.py files
- Add timestamp to target directory name in verl_data_preprocess.py
- Improve error handling and scoring logic in bootcamp_judger.py
- Remove unnecessary comments and update puzzle descriptions in multiple files

internbootcamp/bootcamp/chess/chess.py

@@ -1,6 +1,4 @@
-
-
-### 谜题描述
+"""### 谜题描述
 
 The chess puzzle involves strategically placing a specified number of pieces (e.g., queens, knights) on an N×N grid, adhering to movement constraints inherent to each piece type. The goal is to arrange all pieces such that no two pieces violate predefined interaction rules (e.g., mutual non-attacking positions for queens, restricted adjacency for knights). The solution must satisfy these conditions across the entire grid, regardless of its size or the number of pieces. Each piece's movement capabilities (e.g., queens attacking along rows, columns, and diagonals; knights moving in L-shaped patterns) define the constraints. The puzzle is considered solved when a valid configuration is found for the generalized parameters (any grid size and piece count).