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AI Research
arXiv CS.AI·Jun 9·5 min read
Correct Is Not Enough: Training Reasoning Planners with Executor-Grounded Rewards
TL;DR
Reinforcement learning often relies on final-answer correctness, which can mislead models by rewarding incorrect reasoning paths. To address this, TraceLif was developed to provide executor-grounded rewards that evaluate the quality of reasoning traces.
✦ Why It Matters
Engineers can implement TraceLif to improve the reasoning quality of AI models, leading to more reliable outputs.
Key Takeaways
- Adopt TraceLift for improved reasoning in AI models.
- Utilize TRACELIFT-GROUPS for training datasets.
- Focus on intermediate reasoning quality, not just final answers.
- Implement executor feedback to refine reasoning processes.
How It Works
TraceLift operates by having a planner generate reasoning traces that are tagged and then evaluated by a frozen executor. The executor assesses these traces using a Reasoning Reward Model, which scores them based on quality and usefulness.
This dual feedback mechanism ensures that the reasoning not only leads to correct answers but also supports the model's overall performance in subsequent tasks.