MIRA: Memory-Integrated Reinforcement Learning Agent with Limited LLM Guidance
arXiv:2602.17930v1 Announce Type: cross Abstract: Reinforcement learning (RL) agents often suffer from high sample complexity in sparse or delayed reward settings due to limited prior structure. Large language models (LLMs) can provide subgoal decompositions, plausible trajectories, and abstract priors that facilitate early learning. However, heavy reliance on LLM supervision introduces scalability constraints and dependence on potentially unreliable signals. We propose MIRA (Memory-Integrated Reinforcement Learning Agent), which incorporates a structured, evolving memory graph to guide early training. The graph stores decision-relevant information, including trajectory segments and subgoal structures, and is constructed from both the agent's high-return experiences and LLM outputs. This design amortizes LLM queries into a persistent memory rather than requiring continuous real-time supervision. From this memory graph, we derive a utility signal that softly adjusts advantage estimatio
arXiv:2602.17930v1 Announce Type: cross Abstract: Reinforcement learning (RL) agents often suffer from high sample complexity in sparse or delayed reward settings due to limited prior structure. Large language models (LLMs) can provide subgoal decompositions, plausible trajectories, and abstract priors that facilitate early learning. However, heavy reliance on LLM supervision introduces scalability constraints and dependence on potentially unreliable signals. We propose MIRA (Memory-Integrated Reinforcement Learning Agent), which incorporates a structured, evolving memory graph to guide early training. The graph stores decision-relevant information, including trajectory segments and subgoal structures, and is constructed from both the agent's high-return experiences and LLM outputs. This design amortizes LLM queries into a persistent memory rather than requiring continuous real-time supervision. From this memory graph, we derive a utility signal that softly adjusts advantage estimation to influence policy updates without modifying the underlying reward function. As training progresses, the agent's policy gradually surpasses the initial LLM-derived priors, and the utility term decays, preserving standard convergence guarantees. We provide theoretical analysis showing that utility-based shaping improves early-stage learning in sparse-reward environments. Empirically, MIRA outperforms RL baselines and achieves returns comparable to approaches that rely on frequent LLM supervision, while requiring substantially fewer online LLM queries. Project webpage: https://narjesno.github.io/MIRA/
Executive Summary
The article introduces MIRA, a Memory-Integrated Reinforcement Learning Agent designed to enhance learning efficiency in sparse or delayed reward environments. MIRA leverages a structured memory graph to store decision-relevant information, reducing reliance on continuous Large Language Model (LLM) supervision. This approach amortizes LLM queries into a persistent memory, deriving a utility signal that adjusts advantage estimation to influence policy updates without altering the underlying reward function. Theoretical analysis supports the efficacy of utility-based shaping in early-stage learning, and empirical results demonstrate MIRA's superior performance compared to traditional RL baselines, achieving comparable returns with fewer online LLM queries.
Key Points
- ▸ MIRA uses a structured memory graph to store decision-relevant information.
- ▸ The memory graph is constructed from both high-return experiences and LLM outputs.
- ▸ A utility signal derived from the memory graph influences policy updates.
- ▸ MIRA reduces the need for continuous LLM supervision, improving scalability.
- ▸ Empirical results show MIRA outperforms RL baselines with fewer LLM queries.
Merits
Enhanced Learning Efficiency
MIRA's structured memory graph significantly improves learning efficiency in sparse-reward environments by leveraging both the agent's experiences and LLM-derived priors.
Reduced Reliance on LLM Supervision
By amortizing LLM queries into a persistent memory, MIRA reduces the need for continuous real-time supervision, making it more scalable and less dependent on potentially unreliable LLM signals.
Theoretical and Empirical Validation
The article provides both theoretical analysis and empirical evidence to support the effectiveness of MIRA, demonstrating its superiority over traditional RL baselines.
Demerits
Complexity of Memory Graph Construction
The construction and maintenance of the structured memory graph may introduce additional computational overhead and complexity, which could be a limitation in resource-constrained environments.
Potential Bias from LLM Outputs
Despite reducing reliance on LLM supervision, the initial LLM-derived priors could still introduce biases that might affect the agent's learning process.
Generalization to Different Environments
The effectiveness of MIRA in diverse and dynamic environments remains to be thoroughly tested, as the current study may be limited to specific scenarios.
Expert Commentary
The introduction of MIRA represents a significant advancement in the field of reinforcement learning, particularly in addressing the challenges posed by sparse or delayed reward environments. By integrating a structured memory graph, MIRA effectively amortizes the use of LLM supervision, thereby reducing the computational and scalability constraints associated with continuous LLM queries. The theoretical analysis provided in the article offers a robust foundation for understanding the benefits of utility-based shaping in early-stage learning, while the empirical results demonstrate the practical efficacy of the approach. However, the complexity of maintaining the memory graph and the potential for bias from LLM outputs are important considerations that warrant further investigation. The broader implications of MIRA's approach extend to practical applications in various RL tasks and policy considerations regarding the ethical and efficient use of AI technologies. Overall, MIRA's innovative design and promising results make it a notable contribution to the field, with potential to influence both academic research and industry practices.
Recommendations
- ✓ Further research should explore the scalability and robustness of MIRA in diverse and dynamic environments to ensure its generalizability.
- ✓ Investigations into mitigating potential biases introduced by LLM-derived priors could enhance the reliability and fairness of MIRA's learning process.
