Developed distributed reinforcement learning training enhancements for the ColossalAI repository, focusing on expanding the ColossalChat framework. Implemented support for two new reinforcement learning algorithms, REINFORCE_PPB and RLOO, by updating both the consumer and loss calculation logic to ensure compatibility and correctness within distributed systems. Extended the command-line interface to allow users to select these algorithms, streamlining experimentation and enabling more advanced training workflows. Leveraged Python and machine learning expertise to deliver an end-to-end solution in a single commit, improving flexibility for researchers and engineers working with reinforcement learning at scale. No bug fixes were recorded during this period.