title: Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning
summary: Large language models are typically adapted to downstream tasks through
  supervised fine-tuning on domain-specific data. While standard fine-tuning
  focuses on minimizing generation loss to optimize model parameters, we take a
  deeper step by retaining and leveraging the model's own learning signals,
  analogous to how human learners reflect on past mistakes to improve future
  performance. We first introduce the concept of Mistake Log to systematically
  track the model's learning behavior and recurring errors throughout
  fine-tuning. Treating the original transformer-based model as the Pilot, we
  correspondingly design a Copilot model to refine the Pilot's inference
  performance via logits rectification. We name the overall Pilot-Copilot
  framework the Transformer Copilot, which introduces (i) a novel Copilot model
  design, (ii) a joint training paradigm where the Copilot continuously learns
  from the evolving Mistake Log alongside the Pilot, and (iii) a fused inference
  paradigm where the Copilot rectifies the Pilot's logits for enhanced
  generation. We provide both theoretical and empirical analyses on our new
  learning framework. Experiments on 12 benchmarks spanning commonsense,
  arithmetic, and recommendation tasks demonstrate that Transformer Copilot
  consistently improves performance by up to 34.5%, while introducing marginal
  computational overhead to Pilot models and exhibiting strong scalability and
  transferability.
paper_authors._id: 6833d08edf7cbb5c087a8bf1
paper_authors.name: Jiaru Zou
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-26T02:41:41.249Z
paper_authors._id: 65c288280aa2d53135734a42
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65c288280aa2d53135734a42/5WHmau52EaRS01TOMI3Qg.jpeg
paper_authors.fullname: Jiaru Zou
paper_authors.type: user
paper_authors.user: jiaruz2
paper_authors._id: 6833d08edf7cbb5c087a8bf2
paper_authors.name: Yikun Ban
paper_authors._id: 6833d08edf7cbb5c087a8bf3
paper_authors.name: Zihao Li
paper_authors._id: 6833d08edf7cbb5c087a8bf4
paper_authors.name: Yunzhe Qi
paper_authors._id: 6833d08edf7cbb5c087a8bf5
paper_authors.name: Ruizhong Qiu
paper_authors._id: 6833d08edf7cbb5c087a8bf6
paper_authors.name: Ling Yang
paper_authors._id: 6833d08edf7cbb5c087a8bf7
paper_authors.name: Jingrui He
paper_title: Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning
paper_summary: Large language models are typically adapted to downstream tasks through
  supervised fine-tuning on domain-specific data. While standard fine-tuning
  focuses on minimizing generation loss to optimize model parameters, we take a
  deeper step by retaining and leveraging the model's own learning signals,
  analogous to how human learners reflect on past mistakes to improve future
  performance. We first introduce the concept of Mistake Log to systematically
  track the model's learning behavior and recurring errors throughout
  fine-tuning. Treating the original transformer-based model as the Pilot, we
  correspondingly design a Copilot model to refine the Pilot's inference
  performance via logits rectification. We name the overall Pilot-Copilot
  framework the Transformer Copilot, which introduces (i) a novel Copilot model
  design, (ii) a joint training paradigm where the Copilot continuously learns
  from the evolving Mistake Log alongside the Pilot, and (iii) a fused inference
  paradigm where the Copilot rectifies the Pilot's logits for enhanced
  generation. We provide both theoretical and empirical analyses on our new
  learning framework. Experiments on 12 benchmarks spanning commonsense,
  arithmetic, and recommendation tasks demonstrate that Transformer Copilot
  consistently improves performance by up to 34.5%, while introducing marginal
  computational overhead to Pilot models and exhibiting strong scalability and
  transferability.
paper_ai_summary: The Transformer Copilot framework enhances large language model performance through a Copilot model that refines the Pilot's logits based on a Mistake Log, leading to consistent performance improvements across various benchmarks.
paper_ai_keywords: large language models supervised fine-tuning domain-specific data generation loss model parameters learning signals Mistake Log transformer-based model Copilot model logits rectification joint training paradigm fused inference paradigm performance improvements computational overhead scalability transferability