Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL
Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL is a 7 billion parameter model developed by Jinhe, based on the Qwen2.5-Math-7B architecture. It implements the ReflectRL framework, which learns from 'Golden Negative Trajectories' during on-policy post-training. This model is specifically designed to improve reasoning by using failed expert trajectories as reflective context, gradually transitioning to direct reasoning for inference.
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Overview
ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL is a 7 billion parameter language model developed by Jinhe, built upon the Qwen/Qwen2.5-Math-7B base model. It incorporates the ReflectRL framework, a lightweight approach for learning from Golden Negative Trajectories (GNTs) during on-policy post-training. This method utilizes failed expert trajectories as a reflective context during the training phase, enabling the policy to gradually shift towards direct reasoning for inference.
Key Capabilities
- Enhanced Reasoning: Leverages 'Golden Negative Trajectories' to improve reasoning capabilities.
- Reflective Learning: Uses failed trajectories as a learning context rather than direct imitation.
- On-Policy Post-Training: Optimized through a specific on-policy post-training methodology.
- Lightweight Framework: Implements ReflectRL as a lightweight and efficient learning framework.
Good For
- Research in Reinforcement Learning: Ideal for researchers exploring novel methods for learning from suboptimal or failed trajectories.
- Improving Reasoning Tasks: Potentially beneficial for applications requiring robust reasoning, particularly in mathematical domains given its base model.
- Developing Adaptive AI: Useful for scenarios where models need to learn from diverse experiences, including errors, to refine their decision-making processes.
For more details, refer to the associated paper: ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning.