Papers with RL models

2 papers
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning (D19-1)

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Challenge: Conventional abstractive headline generation methods do not optimize for maximum reader attention.
Approach: They propose a model that generates sensational headlines without labeled data by classifying online headlines with many comments against a summarization model.
Outcome: The proposed model generates sensational headlines without labeled data.
Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models (2026.acl-long)

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Challenge: Reinforcement learning (RL)-based post-training often improves the reasoning performance of large language models beyond the training domain, while supervised fine-tuning (SFT) frequently leads to general capabilities forgetting.
Approach: They propose a feature-level mechanistic analysis methodology to probe RL generalization using a controlled experimental setup.
Outcome: The proposed method identifies a compact, task-agnostic set of features that directly mediate generalization across diverse tasks.

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