AeSlides is a GRPO-based RL framework that uses verifiable aesthetic metrics to optimize LLM slide generation, achieving large gains in layout quality metrics and human scores with only 5K prompts.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Pen-Strategist fine-tunes Qwen-3-14B with RL on a pentesting reasoning dataset and pairs it with a CNN step classifier, reporting 87% better strategy derivation, 47.5% more subtask completions than baselines, and gains on CTFKnow and user studies.
TimeRFT applies reinforcement learning with multi-faceted step-wise rewards and informative sample selection to improve generalization and accuracy in TSFM adaptation beyond supervised fine-tuning.
citing papers explorer
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AeSlides: Incentivizing Aesthetic Layout in LLM-Based Slide Generation via Verifiable Rewards
AeSlides is a GRPO-based RL framework that uses verifiable aesthetic metrics to optimize LLM slide generation, achieving large gains in layout quality metrics and human scores with only 5K prompts.
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Pen-Strategist: A Reasoning Framework for Penetration Testing Strategy Formation and Analysis
Pen-Strategist fine-tunes Qwen-3-14B with RL on a pentesting reasoning dataset and pairs it with a CNN step classifier, reporting 87% better strategy derivation, 47.5% more subtask completions than baselines, and gains on CTFKnow and user studies.
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TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning
TimeRFT applies reinforcement learning with multi-faceted step-wise rewards and informative sample selection to improve generalization and accuracy in TSFM adaptation beyond supervised fine-tuning.