Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-6· 0 citations· 12 references
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.
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.
In the quantitative finance area, particularly in order execution, reinforcement learning (RL) has shown great promise due to its ability to interact with market environments based on real data. However, traditional RL methods suffer from slow research speed and rely on static market assumptions, which do not consider the impact of the agent's execution action on the environment. To address these, we propose a Self-Evolutional single-agent/multi-agent Reinforcement Learning (SE-RL) framework. The framework utilizes a Large Language Model (LLM) to design various RL algorithm modules, such as agent model design, reward function, profiling, communication, and state imagination, by leveraging the LLM generating module output or code. SE-RL could continuously improve the accuracy of LLM-generated RL algorithms through a dual-enhancement kit at both high-level (prompt refinement) and low-level (parameter fine-tuning). Additionally, we use a multi-agent system to simulate dynamic financial markets, accounting for the impact of order executions on market dynamics. To further enhance training in such a dynamic market, we develop a hybrid environment training method that could rebalance each environment's loss weight. Comprehensive experiments on 200 realistic stock datasets demonstrate that our proposed framework outperforms current state-of-the-art baselines. Project page: https://kdd2026-se-rl.github.io/.
Vincent Fu, Xinxin Xu, Weichen Xu et al.· Proceedings of the 32nd ACM...· 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
Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based reinforcement learning (RL) provides a practical optimization paradigm, but its sparse trajectory-level rewards offer limited guidance on intermediate decisions, leaving a supervision gap between episode-level outcomes and token-level policy learning. We propose SEED (SElf-Evolving On-Policy Distillation), a self-evolving framework that converts completed on-policy trajectories into training-time hindsight skills and distills their behavioral effect back into the policy model. SEED first fine-tunes the policy to analyze completed trajectories and generate natural-language skills that capture reusable workflows, decisive observations, or failure-avoidance rules. During RL, the current policy both collects trajectories and serves as the analyzer that extracts hindsight skills from them. Policy updates therefore improve subsequent decision making and skill analysis together, allowing hindsight supervision to evolve with the policy. SEED then re-scores the sampled actions under ordinary and skill-augmented contexts, converting the skill-induced probability shift into a dense token-level on-policy distillation signal. This signal is jointly optimized with outcome-based RL, keeping the auxiliary supervision aligned with the current trajectory distribution. Extensive experiments on text-based and vision-based agentic tasks show that SEED consistently improves performance and sample efficiency, exhibiting robust generalization to unseen scenarios. Our code is available at https://github.com/jinyangwu/SEED.
Jinyang Wu, Shuo Yang, Zhengxi Lu et al.· 5 citations
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 work provides a first look at the AgenticAI-Supervisor platform's core capabilities through a Customer Support Agent case study demonstrating a consistent closed-loop feedback for model optimization, and mitigates reward hacking through rigorous internal state validation and testing.
Akshay Arora, Ishan Nigam, Ashutosh Aggarwal et al.· 0 citations