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cs.LG2026
Structuring MoE Expert Selection for Agentic Reinforcement Learning
Bolian Li, Ting-Yao Hu, Cheng-Yu Hsieh +3
Long-horizon LLM agents are frequently implemented using sparse mixture-of-experts (MoE) models, yet the co-design of agentic behavior and MoE structures remains underexplored. In…
cs.LG2026
RLTLDR: Self-improvement by Internalizing Self-generated Feedback
Michael Kirchhof, Eleonora Gualdoni, Andrew Szot +6
The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards the successful ones. This becom…
cs.LG2023
APoLLo: Unified Adapter and Prompt Learning for Vision Language Models
Sanjoy Chowdhury, Sayan Nag, Dinesh Manocha
The choice of input text prompt plays a critical role in the performance of Vision-Language Pretrained (VLP) models such as CLIP. We present APoLLo, a unified multi-modal approach…