Tagged articles

LLM distillation

3 articles · Page 1 of 1

Machine Learning Algorithms & Natural Language Processing

Jun 16, 2026 · Artificial Intelligence

SFT, DAgger, Offline RL, and OPD: Four Methods Mapped onto a Single 2×2 Grid

The paper shows that SFT, DAgger, offline RL and OPD are the four orthogonal combinations of prefix source (teacher vs. student) and KL direction (forward vs. reverse), exposing three hidden trade‑offs—KL direction, prefix source, and training length—and proposes KL‑mixing and entropy‑gated length curricula that boost Avg@k by 3.6 points, raise Pass@k by up to 5.8 points, and cut response length by three‑fold.

DAggerKL divergenceLLM distillation

0 likes · 17 min read

SFT, DAgger, Offline RL, and OPD: Four Methods Mapped onto a Single 2×2 Grid

Alibaba Cloud Big Data AI Platform

Nov 8, 2024 · Artificial Intelligence

How TAPIR Boosts Small LLMs with Task‑Aware Curriculum Planning

The paper introduces TAPIR, a task‑aware curriculum planning framework that distills instruction‑following abilities from black‑box LLM teachers into smaller student models by filtering difficult prompts, resampling tasks, enhancing response styles, and iteratively optimizing across multiple training rounds, achieving superior performance on benchmark evaluations.

Instruction TuningLLM distillationTAPIR

0 likes · 10 min read

How TAPIR Boosts Small LLMs with Task‑Aware Curriculum Planning

DataFunTalk

Feb 2, 2024 · Artificial Intelligence

Utilizing Negative Samples for Knowledge Distillation of Large Language Models

This paper presents a novel framework that leverages negative samples during large language model distillation through three stages—Negative Assistive Training, Negative Calibration Enhancement, and Adaptive Self‑Consistency—demonstrating significant accuracy gains on challenging mathematical reasoning benchmarks and improved generalization to out‑of‑distribution tasks.

Chain of ThoughtKnowledge TransferLLM distillation

0 likes · 13 min read

Utilizing Negative Samples for Knowledge Distillation of Large Language Models