mlsanityData to 7 Oct 2026

A Gradient Analysis Framework for Rewarding Good and Penalizing Bad Examples in Language Models

arXiv 2408.16751 ↗First posted 29 Aug 2024cs.CL cs.LG stat.MLcited by 1

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Beyond maximum likelihood estimation (MLE), the standard objective of a language model (LM) that optimizes good examples probabilities, many studies have explored ways that also penalize bad examples for enhancing the quality of output distribution, including unlikelihood training, exponential maximizing average treatment effect (ExMATE), and direct preference optimization (DPO). To systematically compare these methods and further provide a unified recipe for LM optimization, in this paper, we present a unique angle of gradient analysis of loss functions that simultaneously reward good examples and penalize bad ones in LMs. Through both mathematical results and experiments on CausalDialogue and Anthropic HH-RLHF datasets, we identify distinct functional characteristics among these methods. We find that ExMATE serves as a superior surrogate for MLE, and that combining DPO with ExMATE instead of MLE further enhances both the statistical (5-7%) and generative (+18% win rate) performance.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 26 of its 38 references that are in the map. First posted on arXiv 29 Aug 2024.

Builds on

26 of 38 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023A General Theoretical Paradigm to Understand Learning from Human Preferences18 Oct
  2. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 24

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  5. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  6. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  7. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  8. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  9. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  10. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  11. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
  12. 2015Sequence Level Training with Recurrent Neural Networkscited by 568
12 more
  1. 2019Neural Text Generation with Unlikelihood Trainingcited by 489
  2. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 375
  3. 2016An Actor-Critic Algorithm for Sequence Predictioncited by 158
  4. 2019Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Trainingcited by 110
  5. 2014word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding methodcited by 109
  6. 2017Adversarial Learning for Neural Dialogue Generationcited by 103
  7. 2020Human-centric Dialog Training via Offline Reinforcement Learningcited by 92
  8. 2024Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Modelscited by 14
  9. 2017Batch Policy Gradient Methods for Improving Neural Conversation Modelscited by 10
  10. 2018Proximal Policy Optimization and its Dynamic Version for Sequence Generationcited by 7
  11. 2018Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluationcited by 6
  12. 2022CausalDialogue: Modeling Utterance-level Causality in Conversationscited by 1

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Citing papers per year: 2025: 1.

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  1. 2025Offline Learning and Forgetting for Reasoning with Large Language Modelscited by 4

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  6. 2026Mutual Debiasing via Dual-Seed Comparison for Probabilistic Sampling in Large Language Models
  7. 2025Proximalized Preference Optimization for Diverse Feedback Types: A Decomposed Perspective on DPO
  8. 2024Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning