TESSY creates stylistically consistent synthetic data via teacher-student token interleaving, yielding 11.25% and 6.68% gains on code benchmarks where pure teacher data causes 3.25% and 10.02% drops.
Gonzalez, Hao Zhang, and Ion Stoica
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 3years
2026 3representative citing papers
A trajectory for student-LLM distillation is better when its tokens are surprising but still high-ranked, and the ratio of average rank to average surprisal (RSR) selects such trajectories better than existing metrics.
OpenCompass is presented as a one-stop, scalable, high-concurrency LLM evaluation platform with modular architecture supporting multiple domains and evaluator types.
citing papers explorer
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How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data
TESSY creates stylistically consistent synthetic data via teacher-student token interleaving, yielding 11.25% and 6.68% gains on code benchmarks where pure teacher data causes 3.25% and 10.02% drops.
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Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment
A trajectory for student-LLM distillation is better when its tokens are surprising but still high-ranked, and the ratio of average rank to average surprisal (RSR) selects such trajectories better than existing metrics.
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OpenCompass: A Universal Evaluation Platform for Large Language Models
OpenCompass is presented as a one-stop, scalable, high-concurrency LLM evaluation platform with modular architecture supporting multiple domains and evaluator types.