feat: enhance SFT training process with new tokenization method, implement custom trainer class for loss computation, and update README with GRPO launcher details for Unsloth LoRA integration

fix: update README with SFT training configuration details, modify modal training scripts to disable assistant-only loss and packing for compatibility, and adjust test assertions to reflect these changes

feat: expand README with synthetic SFT dataset generation instructions, enhance dataset verification and pushing to Hugging Face Hub, and improve modal training scripts with default configurations for curriculum and GPU fallback

feat: introduce reward ablation configurations for enhanced training flexibility, implement YAML loading with extends support, and add reward variant tracking in training scripts

feat: enhance reward configuration management with new logging functions, add parallel Modal training guidelines to documentation, and improve reward config hashing for deterministic behavior

feat: update README with GPU-utilization tuning instructions, enhance modal training script with run name parameter, and modify GRPO configuration for trace logging and vLLM settings

feat: enhance CyberSecurity_OWASP observation model with scenario prompt, improve GRPO batch configuration validation, and add scenario grouping for adaptive difficulty curriculum

feat: add episode trace fingerprinting for improved trace logging and update reward penalties in GRPO configuration

feat: enhance scenario authoring and caching mechanisms, update action submission terminology, and improve reward configuration for CyberSecurity_OWASP environment

feat: enhance training image setup and add startup notice for Modal execution, improve dependency installation process, and implement training heartbeat for monitoring

feat: update training configuration and documentation for Modal execution, including new model integration and enhanced tracking utilities

feat: implement RL environment server with training infrastructure and Modal integration

feat: integrate Trackio for experiment tracking and add Modal training infrastructure with environment and test utilities.

feat: implement core RL training infrastructure and architecture documentation

feat: implement core RL training infrastructure, including GRPO training, evaluation utilities, custom environments, and Modal-based execution scripts.