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arXiv:2410.19920v3 Announce Type: replace Abstract: Reinforcement learning (RL) is a promising approach for aligning large language models (LLMs) knowledge with sequential decision-making tasks. However, few studies have thoroughly investigated the impact on LLM agents capabilities of fine-tuning them with RL in a specific environment. In this paper, we propose a novel framework to analyze the sensitivity of LLMs to prompt formulations following RL training in a textual environment. Our findings reveal that the performance of LLMs degrades when faced with prompt formulations different from those used during the RL training phase. Besides, we analyze the source of this sensitivity by examining the model's internal representations and salient tokens. Finally, we propose to use a contrastive loss to mitigate this sensitivity and improve the robustness and generalization capabilities of LLMs.

arXiv:2509.04733v1 Announce Type: new Abstract: Autoregressive pre-trained models combined with decoding methods have achieved impressive performance on complex reasoning tasks. While mainstream decoding strategies such as beam search can generate plausible candidate sets, they often lack provable coverage guarantees, and struggle to effectively balance search efficiency with the need for versatile trajectories, particularly those involving long-tail sequences that are essential in certain real-world applications. To address these limitations, we propose textsc{CoVeR}, a novel model-free decoding strategy wihtin the conformal prediction framework that simultaneously maintains a compact search space and ensures high coverage probability over desirable trajectories. Theoretically, we establish a PAC-style generalization bound, guaranteeing that textsc{CoVeR} asymptotically achieves a coverage rate of at least $1 - alpha$ for any target level $alpha in (0,1)$.

In this tutorial, we demonstrate how to build an advanced yet accessible Bioinformatics AI Agent using Biopython and popular Python libraries, designed to run seamlessly in Google Colab. By combining sequence retrieval, molecular analysis, visualization, multiple sequence alignment, phylogenetic tree construction, and motif searches into a single streamlined class, the tutorial provides a hands-on approach […] The post How to Create a Bioinformatics AI Agent Using Biopython for DNA and Protein Analysis appeared first on MarkTechPost.