LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation
📰 ArXiv cs.AI
Learn how LARK selects trajectories for efficient reasoning distillation, improving student model performance
Action Steps
- Implement LARK to select trajectories based on learnability
- Evaluate the performance of the student model using the selected trajectories
- Compare the results with existing heuristic-based methods
- Fine-tune the LARK method for specific reasoning distillation tasks
- Apply LARK to real-world applications such as question answering or natural language inference
Who Needs to Know This
Researchers and engineers working on AI model distillation and knowledge graph embedding can benefit from this method to improve the efficiency of their models
Key Insight
💡 LARK prioritizes trajectories based on their learnability by the student model, leading to more efficient reasoning distillation
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🚀 LARK: Efficient reasoning distillation with learnability-grounded trajectory selection! 🤖
Full Article
Title: LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation
Abstract:
arXiv:2605.30651v1 Announce Type: cross Abstract: We study trajectory selection for reasoning distillation, where teacher-generated reasoning trajectories are selectively used as supervision for a student model. Existing methods rely on heuristics such as trajectory quality or model confidence, but they often overlook whether a trajectory is learnable by the student. In this paper, we present LARK, a learnability-grounded method for reasoning trajectory selection. LARK selects trajectories that
Abstract:
arXiv:2605.30651v1 Announce Type: cross Abstract: We study trajectory selection for reasoning distillation, where teacher-generated reasoning trajectories are selectively used as supervision for a student model. Existing methods rely on heuristics such as trajectory quality or model confidence, but they often overlook whether a trajectory is learnable by the student. In this paper, we present LARK, a learnability-grounded method for reasoning trajectory selection. LARK selects trajectories that
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