Papers by Clément Romac
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting (2025.findings-naacl)
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Mohamed Salim Aissi, Clément Romac, Thomas Carta, Sylvain Lamprier, Pierre-Yves Oudeyer, Olivier Sigaud, Laure Soulier, Nicolas Thome
| Challenge: | Reinforcement learning (RL) is a promising approach for aligning large language models knowledge with sequential decision-making tasks. |
| Approach: | They propose to use a contrastive loss framework to analyze the sensitivity of LLMs to prompt formulations following RL training in a textual environment. |
| Outcome: | The proposed framework improves the model's robustness and generalization capabilities by minimizing the model’s internal representations and salient tokens. |