Agents
GENT P
While RAG has many variants, we mainly focus on dense retrievers and categorize them into two types based on their training scheme: (1) training both the retriever and generator in an end-to-end fashion and update the retriever with the language modeling loss (e.g.
MARPLE: A Benchmark for Long-Horizon Inference Emily Jin Zhuoyi Huang
Reconstructing past events requires reasoning across long time horizons.
Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments
Code can be found at https://github.com/nsidn98/LLaMAR
Provable Partially Observable Reinforcement Learning with Privileged Information Yang Cai
Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL).
Policy Learning from Tutorial Books via Understanding, Rehearsing and Introspecting Xiong-Hui Chen
However, current research for decision-making, like reinforcement learning (RL), has primarily required numerous real interactions with the target environment to learn a skill, while failing to utilize the existing knowledge already summarized in the text.
