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Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward

Conference: NeurIPS 2025

arXiv: 2601.19055

Code: None

Area: Code Intelligence

Keywords: user edits, LLM fine-tuning, preference learning, supervised learning, ensemble learning

TL;DR

This paper systematically investigates how to fine-tune LLMs using user-edit data, unifying three feedback types—preference, supervision, and cost—and proposes a simple ensembling procedure that achieves robust adaptation across diverse user distributions.

Background & Motivation

In LLM-based applications such as writing assistants and code agents, users naturally edit model outputs. Such edit data (context + agent response + user edit) constitutes a valuable signal for personalizing LLMs, yet a principled theoretical foundation for exploiting this data remains lacking.

Core challenges:

Unification of multiple feedback types: User-edit data simultaneously encodes preference signals (original vs. edited), supervision labels (edit targets), and cost signals (edit magnitude).

Trade-offs among feedback types: Different feedback types exhibit distinct strengths and weaknesses depending on the user and data distribution.

Distributional robustness: Users' editing styles at test time may differ from those at training time, necessitating robust learning strategies.

Method

Overall Architecture

Given a user-edit dataset \(\{(c_i, y_i^{\text{agent}}, y_i^{\text{edit}})\}\), the paper studies three learning paradigms: - Preference learning: treats \((y^{\text{agent}}, y^{\text{edit}})\) as a preference pair and optimizes via DPO/RLHF. - Supervised learning: directly performs SFT with \(y^{\text{edit}}\) as the target. - Cost learning: uses edit distance as a cost signal to minimize expected cost.

Key Designs

1. Theoretical Analysis of Three Feedback Types

Error bounds are derived for each learning method: - Preference learning: suitable when user edits are sufficient and diverse, but sensitive to edit quality. - Supervised learning: performs well when edits closely approximate the optimal output, but may overfit to specific editing styles. - Cost learning: leverages edit-magnitude information more finely, but requires an accurate cost function.

2. Ensembling Procedure

  • Evaluates the three methods on multiple validation tasks.
  • Optimally mixes the three strategies via convex combination weights \(\alpha\): \(\pi_{\text{ensemble}} = \alpha_1 \pi_{\text{pref}} + \alpha_2 \pi_{\text{sup}} + \alpha_3 \pi_{\text{cost}}\)
  • Weights are determined automatically by minimizing the ensemble loss on a validation set.
  • Simple yet effective, adapting to diverse user–data distribution scenarios.

3. Theoretical Trade-off Analysis

  • Proves that each method holds advantages under different assumptions and that no single method is universally optimal.
  • The performance bound of the ensemble method adaptively approaches the best of the three constituent methods.

Loss & Training

  • Preference learning: \(\mathcal{L}_{\text{pref}} = -\mathbb{E}[\log \sigma(r(y^{\text{edit}}) - r(y^{\text{agent}}))]\)
  • Supervised learning: \(\mathcal{L}_{\text{sup}} = -\mathbb{E}[\log \pi(y^{\text{edit}} | c)]\)
  • Cost learning: \(\mathcal{L}_{\text{cost}} = \mathbb{E}[d(y, y^{\text{edit}}) \cdot \nabla \log \pi(y | c)]\)

Key Experimental Results

Main Results

Evaluation results across two domains (following the setup of Gao et al. 2024):

Writing assistant task (Win Rate vs. Reference):

Method Light-edit Users Medium-edit Users Heavy-edit Users Overall
SFT-only 55.2% 48.3% 42.1% 48.5%
DPO 52.8% 51.7% 50.3% 51.6%
Cost-based 50.1% 52.4% 53.8% 52.1%
Ensemble (Ours) 56.3% 54.1% 53.2% 54.5%

Code agent task (Pass@1):

Method Simple Modifications Refactoring Edits Mixed Scenarios
SFT-only 72.1% 45.3% 56.8%
DPO 68.5% 51.2% 58.3%
Cost-based 65.3% 49.8% 56.1%
Ensemble (Ours) 71.8% 53.6% 61.2%

Ablation Study

Analysis of ensemble weight variation across user types:

User Type \(\alpha_{\text{pref}}\) \(\alpha_{\text{sup}}\) \(\alpha_{\text{cost}}\)
Light-edit 0.15 0.72 0.13
Medium-edit 0.38 0.35 0.27
Heavy-edit 0.45 0.12 0.43

Key Findings

  1. No single feedback type is optimal across all scenarios, corroborating the theoretical analysis.
  2. SFT performs best for light-edit users (where edits approximate optimal outputs); DPO is more robust for heavy-edit users.
  3. The ensemble method approaches or achieves optimality in all scenarios, demonstrating strong adaptability.
  4. Cost learning is particularly valuable in scenarios with high variance in edit magnitude.

Highlights & Insights

  • Unified perspective: The first work to theoretically unify the analysis of three feedback types present in user-edit data.
  • Practical orientation: User edits arise naturally in real-world applications, making the proposed methods directly deployable.
  • Simplicity and effectiveness: The ensembling procedure requires no complex architectural changes and adapts to diverse scenarios through weight adjustment alone.

Limitations & Future Work

  1. The ensembling procedure requires a validation set for weight tuning, which may be unstable under data scarcity.
  2. The current framework assumes user edits always improve model outputs, without accounting for erroneous edits.
  3. Theoretical analysis relies on linear or low-complexity assumptions, which may not fully reflect the behavior of practical deep models.
  4. Validation is limited to two domains; generalizability remains to be explored.
  • RLHF/DPO: Standard preference learning methods for LLMs.
  • Gao et al. 2024: Pioneering work on user edits as learning signals.
  • Ensemble learning: Simple model combination frequently yields surprisingly strong performance in practice.

Rating

  • ⭐ Novelty: 8/10 — First to theorize user-edit learning and unify three feedback types.
  • ⭐ Value: 8/10 — Directly relevant to applications such as writing assistants and code agents.
  • ⭐ Writing Quality: 8/10 — Theory and experiments are well integrated.