Mitigating Scaffolding Collapse in Socratic Tutors via Representation Alignment
Quick Answer
The proposed Scaffold-Preserving Representation Alignment framework enhances Socratic tutoring by reducing the collapse rate to 32% on Qwen3-8B, delaying collapse onset beyond nine turns, and maintaining low over-refusal rates, thereby improving long-horizon tutoring robustness against red-teaming attacks.
Key Points
- Introduces a two-stage framework for Socratic tutors to prevent scaffolding collapse.
- Combines supervised fine-tuning with trajectory-weighted preference optimization.
- Achieves a 32% collapse rate and delays onset beyond nine dialogue turns.
- Evaluated across five STEM disciplines and various red-teaming strategies.
- Demonstrates improved robustness of long-horizon Socratic tutoring.
Paper Resources
Source Excerpt
(LLM)-based Socratic tutors increasingly guide students through multi-turn questioning, but they can suffer from scaffolding collapse: under sustained student pressure, a tutor gradually abandons guided inquiry and reveals solutions directly. Prior defenses primarily constrain observable responses through prompting, preference optimization, or filtering, leaving the internal representation drift that precedes trajectory-level collapse largely unaddressed. We propose Scaffold
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