* v0
* 2 gpu setup
* improve parsing from yaml
* update yaml dataset example
* remove restriction on flash attn
* more comments
* first version of the readme
* pin torch
* simplify requirements
* just flash attn
* use set env instead
* simpler set env
* readme
* add wandb project to setup
* update template
* update model id
* post init to capture the config and weight
* extract metadata
* update config
* update dataset config
* move env for wandb project
* pre-commit
* remove qwen-math from training
* more instructions
* unused import
* remove trl old
* warmup ratio
* warmup ratio
* change model id
* change model_id
* add info about CUDA_VISIBLE_DEVICES
* feat: Add optional curriculum support to dataset registration and creation
* docs: Add docstrings to create_curriculum() and register_dataset()
* feat: Add curriculum configuration classes for CurriculumExperiment
* feat: Add weight parameter to CurriculumAttributeConfig and use in DatasetSpec
* refactor: Simplify CurriculumAttributeConfig with "*" attribute level support
* test: Add unit tests for CurriculumExperiment class
* feat: Add from_yaml() method to CurriculumExperimentConfig with unit test
* feat: Add initial server structure with configuration, registry, and middleware
* feat: Add chain_sum dataset to experiment registry test
* fix: Update test_registry to use DatasetSpec for composite config validation
* refactor: Update Pydantic config to use json_schema_extra and ConfigDict
* feat: Add Pydantic models for API request/response data
* feat: Implement basic experiment management endpoints with tests
* feat: Implement composite configuration endpoints for experiments
* fix: Add missing DatasetConfigUpdate import in server.py
* refactor: Update dataset config update method to properly merge config updates
* fix: Correctly retrieve current dataset config in composite endpoint
* feat: Add basic CLI structure with experiments and config commands
* feat: Add initial CLI tool with basic experiment management commands
* refactor: Reorganize CLI package structure and fix import paths
* refactor: Implement initial CLI commands for experiment management
* feat: Implement HTTP client for Reasoning Gym server in RGC CLI tool
* fix: Move print statements inside try block to resolve SyntaxError
* fix: Resolve SyntaxError in edit_config function by adding missing except block
* feat: Add default app instance in server module for easier uvicorn startup
* docs: Add README.md with server and RGC tool documentation
* remove unused files
* refactor: Remove unsupported type annotation in registry.py
* refactor: Move ExperimentRegistry to coaching module and add Experiment class
* fix: Add missing CompositeDataset import in test_registry.py
* refactor: Implement lazy ASGI app creation for server initialization
* feat: Add health check command to RGC CLI for server connection
* feat: Add version tracking support to CompositeDataset
* feat: Add DatasetVersionManager for tracking dataset versions
* feat: Add entry_id metadata and score_answer_with_id method to CompositeDataset
* feat: Add entry_id metadata combining version and index
* fix: Resolve undefined variable by storing version_id before use
* test: Add comprehensive unit tests for score_answer_with_id() function
* test: Add comprehensive version tracking test for dataset config updates
* feat: Validate dataset weights are positive in CompositeDataset initialization
* feat: Add weight update and normalization methods to CompositeDataset
* refactor: Centralize weight normalization in CompositeDataset and allow zero-weight datasets
* feat: Add negative weight validation to CompositeDataset constructor
* feat: Add duplicate dataset name check in CompositeDataset and update test
* refactor: Move duplicate dataset name check inside dataset iteration loop
* refactor: Update CompositeDataset weight management to use config as source of truth
* refactor: Move duplicate dataset name check to CompositeConfig.validate()
* test: Update composite dataset weight test assertions and validation
* feat: Add methods to add and remove datasets in CompositeDataset
* refactor: Remove weight normalization and use unnormalized weights directly
* refactor: Remove redundant total weight check in update_dataset_weights
* feat: Add batch generation and scoring endpoints to server
* fix: Import BatchEntry in server.py to resolve undefined name error
* refactor: Update ReasoningGymDataset to use server for batch generation and scoring
* fix: Add missing List and Dict type imports
* feat: Add get_batch() and score_outputs() methods to RGClient
* test: Add unit tests for generate_batch and score_outputs endpoints
* refactor: Add DatasetVersionManager to Experiment class and CompositeDataset constructor
* feat: Add validation for base_index and batch_size in generate_batch endpoint
* refactor: Remove unused BatchRequest type from imports
* refactor: Convert models to use Pydantic exclusively
* test: Update scoring endpoint tests to use correct request model format
* refactor: Rename ScoreItem to AnswerItem and update related code
* feat: Update scoring endpoint to return ordered ScoringResponse with scores and entry_ids
* fix: Add missing ScoringResponse import in server.py
* move verl ppo sample with server into own file
* refactor: Use Pydantic models for get_batch() and score_outputs() in RGClient
* refactor: Update client methods to use Pydantic models for type safety
* refactor: Use Pydantic models for experiment and dataset config operations
* refactor: Clean up duplicate methods and improve error handling in main.py
* first bits of rg server use for verl
* refactor: Optimize scoring with single HTTP request in _score_output
* fix: Correct experiment creation with ExperimentCreate object
* grpo tests with server
