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While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context limits. To address this, sequential approaches like MemAgent extend the effective context by reading text in segments and iteratively updating a fixed-size memory. However,...
STAR is proposed, an efficient PbRL method that integrates preference margin regularization and policy regularization that improves feedback efficiency and facilitates more robust reward and value function learning.
HyMem is a hierarchical framework that explicitly separates the agent's context into distinct functional layers to separate high-level planning from execution and complex analysis, allowing the model to maintain focus and accuracy across complex, long-horizon tasks.
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