Narjes Nourzad

2 accepted papers

2026

MIRA: Memory-Integrated Reinforcement Learning Agent with Limited LLM Guidance

ICLR 2026poster

Reinforcement learning (RL) agents often face high sample complexity in sparse or delayed reward settings, due to limited prior knowledge. Conversely, large language models (LLMs) can provide subgoal structures, plausible trajectories, and abstract priors that support early learning. Yet heavy relia…

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2026

Memory Based Advantage Shaping for LLM-Guided Reinforcement Learning (Student Abstract)

Narjes Nourzad, Carlee Joe-Wong

AAAI 2026technical

In environments with sparse or delayed rewards, reinforcement learning (RL) incurs high sample complexity due to the large number of interactions needed for learning. This limitation has motivated the use of large language models (LLMs) for subgoal discovery and trajectory guidance. While LLMs can s

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