We introduce ReasonFlux-PRM, a trajectory-aware process reward model (PRM) explicitly designed to evaluate the trajectory-response type of reasoning traces. ReasonFlux-PRM incorporates both step-level and trajectory-level supervision, enabling fine-grained reward assignment aligned with structured chain-of-thought data. ReasonFlux-PRM is able to support both offline and online reward supervision, by selecting high-quality training data for model distillation, providing dense process-level rewards for policy optimization during reinforcement learning, and enabling reward-guided test-time scaling. Model Type Size Capabilities Use Cases Download ReasonFlux-PRM PRM 7B • Trajectory-aware scoring• Online/Offline supervision• Dense process rewards Data selection, RL training, Test-time scaling 🤗 7B ReasonFlux-PRM PRM 1.5B • Lightweight scoring• Efficient inference• Edge deployment Resource-constrained applications 🤗 1.5B ReasonFlux-PRM-Qwen-2.5 End-to-End Trained Policy Model 7B • Long CoT reasoning • Solving complex tasks and problems Math and Science Reasoning 🤗 7B
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: Gen-Verse
Теги: qwen2, conversational, text-generation-inference, endpoints_compatible
Лайков: 3 | Загрузок: 33
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
