hello-diana / MASCOT

What it does

• Two-stage framework. persona-aware behavioral alignment of individual Speakers, followed by collaborative dialogue optimization of the full group.
• Low-resource bi-level optimization. Speaker and Director policies train with LoRA adapters only (base weights are referenced by Hugging Face name). Reward-model checkpoints and explicitly merged checkpoints may contain full model weights —
• Director orchestration. a Director agent selects who speaks next and issues natural-language directives specifying *speaker, action, and tone*. The Director is optimized with GRPO (the paper's method); an offline DPO trainer is retained as
• Self-contained pipeline. episode generation, rubric judging, preference-data construction, reward modeling, GRPO/DPO training, and evaluation, all against an OpenAI-compatible vLLM server.