Papers with reward modules

2 papers
Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification (2026.acl-long)

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Challenge: Existing large language model-based readability control methods rely on pre-labeled sentence corpora and primarily target English.
Approach: They propose a framework for adaptive multilingual text simplification without parallel corpora supervision that integrates three reward modules: vocabulary coverage, semantic preservation, and coherence.
Outcome: The proposed framework achieves higher lexical coverage at target proficiency levels while maintaining original meaning and fluency compared to stronger LLMs.
CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks (2026.acl-long)

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Challenge: Existing methods for MLLMs are weak on explicit attacks, but weak on implicit ones.
Approach: They propose an automated red-teaming pipeline that leverages reinforcement learning with tailored reward modules to generate diverse implicit samples across 14 domains.
Outcome: The proposed method outperforms existing methods in implicit and explicit attacks while maintaining high utility.

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