An E2E Framework to Improve Agentic AI Using GRPO Reasoning and Continuous Memory Based Learning

Abstract

This work proposes a reinforcement learning-based framework for fine-tuning and optimizing Small Language Models (SLMs) used in Agentic AI systems, with a focus on improving tool invocation and decision-making over complex, multi-step tasks. The framework targets both open-source and proprietary language models operating in interactive environments that integrate multiple data sources and external APIs through autonomous agents. We leverage Grouped Relative Policy Optimization (GRPO) to generate and evaluate multiple complete execution trajectories per query within a simulated interaction environment, assigning trajectory-level rewards through an autonomous LLM-based evaluation module (RULER). High-reward trajectories are used to iteratively update SLM parameters, producing a compact, specialized policy that improves tool selection accuracy, response quality, and robustness under dynamic query distributions. To address the limitations of fine-tuning proprietary models, we introduce a memory-based middleware that stores query-trajectory-reward tuples and reuses high-reward trajectories at inference time for semantically similar queries, enabling trajectory-level adaptation without modifying underlying model parameters. A learned reuse policy governs whether to replay, adapt, or regenerate trajectories, yielding substantial reductions in inference latency. Overall, the proposed architecture combines GRPO-based policy refinement with learned trajectory reuse, improving accuracy and efficiency while reducing reliance on expensive large-model retraining and preserving deployment flexibility across Agentic AI use cases such as database querying, web search, and multi-tool orchestration. We further validate the framework on a multi-agent information-seeking task involving enterprise email search (Enron corpus) with dynamic redirection to web search via SearXNG, demonstrating generalization beyond incident-management workflows.