Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems

1. 1Identify hard reasoning problems that are challenging for LLMs to solve
2. 2Transform these problems into cognitively simpler variants through task reformulation
3. 3Use reinforcement learning from verifiable rewards (RLVR) to learn from the reformulated problems
4. 4Evaluate and refine the model's performance on the original hard problems

ML researchers and AI engineers can benefit from this approach to improve the reasoning abilities of LLMs, and product managers can apply this to develop more effective AI-powered products

💡 Adaptive reformulation of instances enables learning from problems that are too difficult to solve under the current policy

🤖 Cog-DRIFT helps LLMs learn from hard reasoning problems by simplifying them