arXiv Artificial Intelligence

Distilling LLM Reasoning into Graph of Concept Predictors

2 hours ago 02.10.2026 07:00 👁 2 views
Distilling LLM Reasoning into Graph of Concept Predictors

Quick summary

arXiv:2602.03006v3 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active distillation reduces these costs by querying an LLM oracle to train small discriminative students, but most pipelines distill only final labels, discarding intermediate reasoning signals and offering limited diagnostics of what reasoning is missing and where errors arise. We propose Graph of Concept Predictors (GCP), a reasoning-aware active distillation framework in which the teacher's reasoning i

Key takeaways

  • arXiv:2602.03006v3 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale.
  • Active distillation reduces these costs by querying an LLM oracle to train small discriminative students, but most pipelines distill only final labels, discarding intermediate reasoning signals and offering limited diagnostics of what reasoning is missing and where errors arise.
  • We propose Graph of Concept Predictors (GCP), a reasoning-aware active distillation framework in which the teacher's reasoning i

Why it matters

“Distilling LLM Reasoning into Graph of Concept Predictors” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗