August 2025 monthly summary for repository alibaba/ROLL focusing on key features delivered, major fixes, and impact. The month highlights performance-focused refactor of reward normalization configuration to improve training flexibility and experimentation speed. Overview: - Key features delivered and scope: Refactored reward normalization across multiple algorithm configuration files by introducing granular norm_mean_type and norm_std_type, replacing legacy reward_norm, reward_shift, and reward_scale. This enables finer control over normalization during training and supports easier experimentation. - Major bugs fixed: No significant bugs reported in this period; no critical fixes required beyond ongoing maintenance. - Overall impact and accomplishments: Improved training stability and flexibility, accelerated experimentation cycles, and better alignment between configuration parameters and training outcomes. The change reduces configuration drift and lowers the barrier to iterating on reward normalization strategies. - Technologies/skills demonstrated: Python refactoring, configuration design, multi-file coordination, and Git-based traceability (commit references).