This research bridges theoretical foundations of reinforcement learning and graph-based memory with autonomous agent workflows, and offers a practical, scalable reference framework for developing artificial intelligence technologies in complex, multi-step autonomous systems.
Abstract
This paper addresses key technical challenges in current large language model (LLM) agent applications, including long-horizon planning, sparse reward attribution, and dynamic environmental interaction, by designing and optimizing an intelligent agent workflow. The proposed architecture is based on the synthesis of core AI paradigms: Visual, Language, Generative, Graph, Multimodal, Reinforcement, and Agent Intelligence. Unlike conventional baseline models that rely on static prompting and lack robust perception-action loops, our approach introduces a Partially Observable Markov Decision Process (POMDP) routing mechanism. This mechanism is augmented with an internal, self-correcting reward model that evaluates decision trajectories before execution. By integrating multimodal inputs and advanced reinforcement learning principles (such as proximal policy optimization and value function approximation), the agent maintains long-term structural memory and dynamically adapts its reasoning pathways to mitigate error accumulation. Empirical experiments on the ALFWorld embodied simulation environment and the WebShop online navigation benchmark demonstrate a 24.5% absolute improvement in task success rate and trajectory efficiency over mainstream baselines like the standard ReAct framework. Comprehensive ablation studies confirm the significant contribution of the reward-driven critique module in suppressing hallucination rates. This research bridges theoretical foundations of reinforcement learning and graph-based memory with autonomous agent workflows. Ultimately, the resulting architecture offers a practical, scalable reference framework for developing artificial intelligence technologies in complex, multi-step autonomous systems. Code is available at https://github.com/01Amez/RLAW_Implementation.
This work introduces AgentSearch, a cost-aware Monte Carlo Tree Search (MCTS) framework that constructs agentic workflows through deliberative lookahead search and attains single-episode success while reducing computational costs by up to 47%, thereby eliminating the trial-and-error exploration required by previous adaptive methods.
D. Attota, Ying Xie· Proceedings of the 15th Inte...· 0 citations
This paper introduces an LLM-Augmented Reinforcement Learning Agent that integrates LLM-driven planning with RL-based action optimization, and highlights a promising direction for building more capable autonomous systems.
Christophe D. Hounwanou, John Emeka Eze, Yaé Ulrich Gaba· 0 citations
Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and verbal goals. While recent vision-language models (VLMs) excel at static perception tasks, they struggle in interactive environments. Reinforcement learning (RL) offers a natural way to address this limitation, yet online RL approaches suffer from costly interaction and sparse rewards in embodied settings. This paper introduces ORBIT , an O n-policy R einforcement fine-tuning (RFT) framework with offline rewards for Em B od I ed T ask Planning, that preserves the generalization benefits of RFT while addressing the challenges of costly interaction and sparse rewards, supported by solid theoretical guarantees. Our approach is evaluated on EmbodiedBench, a recent benchmark for interactive embodied tasks, covering both in-domain and out-of-domain scenarios. Experimental results show that ORBIT achieves SOTA performance on EB-ALFRED, outper-forming all closed-source and online-RL-based methods, while being substantially more effi-cient in training speed and computational cost, remaining robust to sub-optimal expert trajectories, and exhibiting strong generalization to unseen environments. We released all code and data at https://github.com/mail-taii/Reinforced-Reasoning-for-Embodied-Planning
Di Wu, Jiaxin Fan, Chloe Gu et al.· Annual Meeting of the Associ...· 0 citations
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.
Subhadip Mandal, Shalini Mittal, Sukanya Bag et al.· 2026 6th International Confe...· 0 citations
ODYSSE is presented, a Reinforced Fine-Tuning (RFT) framework for personalized agentic reasoning designed to address long action horizons and strong cross-step dependencies in personalized agentic reasoning, and an episodic batch sampler that groups actions from the same episode into unified training batches, facilitating coherent optimization under ESPO.
Jiaqi Zhang, Tong Chen, Junliang Yu et al.· 0 citations
Group Planning-aware Policy Optimization (PlanPO) is proposed, a simple yet effective RL method for learning generalizable planning abilities beyond task-specific high-quality behavior patterns that enables agents to actively learn generalizable and deliberate behaviors spanning interaction planning and textual generation from high-quality rollouts, without degenerating into vanilla length minimization.
D. Liang, Liyuan He, Xuan Feng et al.· 0 citations
