{ "id": "interactive-training", "title": "Controlling a running training job with humans and AI agents", "eyebrow": "Feedback-driven neural network optimization", "description": "Interactive Training connects live metrics to human and LLM interventions in running neural-network jobs; v2 adds typed controls and an ordered action journal.", "lead": "Interactive Training: Feedback-Driven Neural Network Optimization, by Wentao Zhang, Yang Young Lu, and Yuntian Deng, makes an ongoing neural-network training process controllable by humans and AI agents. First released on October 2, 2025 and published at EMNLP 2025 System Demonstrations, it exposes optimizer settings, training data, and checkpoints through a control server. Interactive Training 2 extends this approach to a reusable protocol for live training, released on arXiv on July 17, 2026.", "original_papers": [ "interactive-training-2025", "interactive-training-2026" ], "terms": [ "interactive training", "feedback-driven optimization", "LLM training controller", "live hyperparameter tuning", "human-in-the-loop training", "closed-loop training control" ], "sections": [ { "id": "running-process", "title": "Make the running process the object of control", "paragraphs": [ "A training dashboard shows loss curves and evaluations. Interactive control connects those observations to changes in the process that is producing them. A researcher sees instability, changes the learning rate, and observes subsequent updates in the same run. An LLM controller uses the same feedback cycle: inspect evidence, request an intervention, and inspect its effect.", "This differs from choosing a configuration before launch or proposing a new experiment after a run finishes. The active model and optimizer continue while exposed controls change. A checkpoint restore or a new round is a separate operation with its own state transition; the interface should record which operation actually occurred." ], "citations": [ "interactive-training-2025", "interactive-training-2026" ] }, { "id": "original-system", "title": "The 2025 system: monitoring connected to intervention", "paragraphs": [ "The original framework uses a control server to mediate between a human or automated agent and the training process. Its Trainer integration exposes commands for optimizer hyperparameters, data handling, and checkpoints. A live interface lets the controller observe training and submit changes instead of editing a static recipe and relaunching the program.", "The paper presents three case studies spanning training stability, sensitivity to initial hyperparameters, and changing user needs. Its LLM-driven learning-rate example demonstrates an agent reading feedback and intervening during optimization. The system contribution is the connection among live observation, a control interface, and the training loop—not a replacement for the optimizer’s parameter updates." ], "citations": [ "interactive-training-2025" ] }, { "id": "shared-protocol", "title": "The 2026 extension: one protocol for different controllers", "paragraphs": [ "Interactive Training 2, by Wentao Zhang, Xuanhe Pan, Han Zhou, Yang Lu, and Yuntian Deng, replaces a fixed Trainer-specific command vocabulary with registered settings and actions. A TrainingSession exposes typed controls and descriptions. Humans, scripts, heuristics, and LLM agents submit requests through the same interface.", "The training application chooses control points at which queued requests are validated and applied. An ordered journal connects requests to execution results, evaluations, checkpoints, and controller decisions. The Aim workspace brings this record together with live metrics. The paper also distinguishes within-round interventions from cross-round planning: a new round starts a fresh model and optimizer, while a textual journal carries observations and reflections forward." ], "citations": [ "interactive-training-2026" ] }, { "id": "research-context", "title": "Compare mechanisms, controllers, and experiments", "paragraphs": [ "LiveTune predates Interactive Training and exposes LiveVariables for changing optimization parameters without restarting the loop. It supplies an earlier mechanism for live parameter adjustment. Interactive Training’s focus is the human and AI-agent workflow around training, and v2 makes the control vocabulary and event record reusable across applications.", "AI Training Manager studies a bounded LLM supervisor that asynchronously reads telemetry and adjusts a running recipe. Its experiments introduce specific failure modes in supervised learning and reinforcement learning. This is a useful comparison at the controller level: what evidence reaches the agent, which actions it can request, and how later outcomes affect its decisions. Infrastructure support and measured controller performance are distinct contributions." ], "citations": [ "livetune", "ai-training-manager", "interactive-training-2025", "interactive-training-2026" ] }, { "id": "design-loop", "title": "Design an observable feedback loop", "paragraphs": [ "Expose controls that the training application can actually apply, and define when changes take effect. Record the observation that motivated a request, the requested value, the applied value, the step of application, and the subsequent measurements. A submitted request and a successful update are different events. Keeping them linked makes the behavior inspectable.", "Evaluate an automated controller against a stated training objective under explicit workloads and budgets. Count its inference and intervention overhead, preserve the run history, and separate improvements within a continuing run from results of newly initialized rounds. The Interactive Training papers and implementation provide the machinery for this feedback loop; the controller and task determine what the loop accomplishes." ], "citations": [ "interactive-training-2025", "interactive-training-2026", "ai-training-manager" ] } ], "related_guides": [ "llm-decision-trees" ], "demo": "training", "reading_order": [ "interactive-training-2025", "interactive-training-2026", "livetune", "ai-training-manager" ], "updated_at": "2026-11-10", "url": "https://yuntiandeng.com/research/interactive-training/", "papers": [ { "id": "interactive-training-2025", "title": "Interactive Training: Feedback-Driven Neural Network Optimization", "authors": [ { "given": "Wentao", "family": "Zhang" }, { "given": "Yang Young", "family": "Lu" }, { "given": "Yuntian", "family": "Deng" } ], "year": 2025, "venue": "EMNLP 2025 System Demonstrations", "paper_url": "https://aclanthology.org/2025.emnlp-demos.65/", "pdf_url": "https://aclanthology.org/2025.emnlp-demos.65.pdf", "code_url": "https://github.com/yuntian-group/interactive-training", "doi": "10.18653/v1/2025.emnlp-demos.65", "summary": "An open-source framework in which humans and AI agents intervene in ongoing neural-network training through a control server, changing optimizer settings, training data, and model checkpoints.", "connection": "The original Interactive Training system connects live monitoring with feedback-driven control of a running training process.", "bibtex_type": "inproceedings", "citation_key": "zhang-etal-2025-interactive", "source_urls": [ "https://aclanthology.org/2025.emnlp-demos.65/", "https://arxiv.org/html/2510.02297v1" ], "arxiv_id": "2510.02297", "arxiv_first_posted": "2025-10-02", "abstract": "Traditional neural network training typically follows fixed, predefined optimization recipes, lacking the flexibility to dynamically respond to instabilities or emerging training issues. In this paper, we introduce Interactive Training, an open-source framework that enables real-time, feedback-driven intervention during neural network training by human experts or automated AI agents. At its core, Interactive Training uses a control server to mediate communication between users or agents and the ongoing training process, allowing users to dynamically adjust optimizer hyperparameters, training data, and model checkpoints. Through three case studies, we demonstrate that Interactive Training achieves superior training stability, reduced sensitivity to initial hyperparameters, and improved adaptability to evolving user needs, paving the way toward a future training paradigm where AI agents autonomously monitor training logs, proactively resolve instabilities, and optimize training dynamics.", "abstract_source": "https://arxiv.org/html/2510.02297v1", "updated_at": "2026-11-10" }, { "id": "interactive-training-2026", "title": "Interactive Training 2: Auditable Control Plane for Live Model Training", "authors": [ { "given": "Wentao", "family": "Zhang" }, { "given": "Xuanhe", "family": "Pan" }, { "given": "Han", "family": "Zhou" }, { "given": "Yang", "family": "Lu" }, { "given": "Yuntian", "family": "Deng" } ], "year": 2026, "venue": "arXiv", "paper_url": "https://arxiv.org/abs/2607.18314", "pdf_url": "https://arxiv.org/pdf/2607.18314", "code_url": "https://github.com/yuntian-group/interactive-training", "doi": "10.48550/arXiv.2607.18314", "summary": "A reusable protocol with typed settings, structured actions, application-selected control points, and an ordered journal shared by humans, scripts, and LLM controllers.", "connection": "Extends the 2025 framework from a Trainer-specific command interface to a common control protocol across training workflows.", "bibtex_type": "article", "citation_key": "zhang2026interactivetraining2", "source_urls": [ "https://arxiv.org/abs/2607.18314", "https://arxiv.org/html/2607.18314v1", "https://github.com/yuntian-group/interactive-training" ], "equal_contribution": [ "Wentao Zhang", "Xuanhe Pan", "Han Zhou" ], "arxiv_id": "2607.18314", "arxiv_first_posted": "2026-07-17", "publication_type": "preprint", "container_title": "arXiv", "abstract": "Experiment trackers show how training is progressing, but changing a live run still usually requires trainer-specific code. We present Interactive Training 2, an open-source control plane for steering training through a shared protocol. Training applications declare which settings and actions they expose, humans and automated controllers submit requests through the same interface, and the training loop validates and applies them at safe control points. A customized Aim workspace combines live metrics and controls with a chronological record of requests and outcomes. We demonstrate the system across five NLP and reinforcement-learning workflows. The released code and traces provide a reusable foundation for auditable human- and agent-guided training.", "abstract_source": "https://arxiv.org/html/2607.18314v1", "updated_at": "2026-11-10" }, { "id": "livetune", "title": "LiveTune: Dynamic Parameter Tuning for Feedback-Driven Optimization", "authors": [ { "given": "Soheil Zibakhsh", "family": "Shabgahi" }, { "given": "Nojan", "family": "Sheybani" }, { "given": "Aiden", "family": "Tabrizi" }, { "given": "Farinaz", "family": "Koushanfar" } ], "year": 2023, "venue": "arXiv", "paper_url": "https://arxiv.org/abs/2311.17279", "pdf_url": "https://arxiv.org/pdf/2311.17279", "doi": "10.48550/arXiv.2311.17279", "summary": "LiveVariables expose optimization parameters through designated system ports for real-time adjustment without restarting the optimization loop.", "connection": "An earlier system for changing running optimization processes; compare its parameter mechanism with Interactive Training’s human/agent command interface and v2 protocol.", "bibtex_type": "article", "citation_key": "shabgahi2023livetune", "source_urls": [ "https://arxiv.org/abs/2311.17279", "https://arxiv.org/html/2311.17279v2" ], "arxiv_id": "2311.17279", "arxiv_first_posted": "2023-11-28", "publication_type": "preprint", "container_title": "arXiv", "updated_at": "2026-11-10" }, { "id": "ai-training-manager", "title": "AI Training Manager: Bounded Closed-Loop Control of Adaptive Training Recipes", "authors": [ { "given": "Anjali", "family": "Rao" }, { "given": "Nikhil Kamalkumar", "family": "Advani" } ], "year": 2026, "venue": "arXiv", "paper_url": "https://arxiv.org/abs/2606.29871", "pdf_url": "https://arxiv.org/pdf/2606.29871", "doi": "10.48550/arXiv.2606.29871", "summary": "An asynchronous LLM supervisor observes training telemetry and proposes constrained changes to a running training recipe, with experiments on deliberately induced supervised-learning and reinforcement-learning failure modes.", "connection": "Studies an automated controller over active training, complementing Interactive Training’s reusable infrastructure for human and automated controllers.", "bibtex_type": "article", "citation_key": "rao2026aitrainingmanager", "source_urls": [ "https://arxiv.org/abs/2606.29871", "https://arxiv.org/html/2606.29871v3" ], "arxiv_id": "2606.29871", "arxiv_first_posted": "2026-06-29", "publication_type": "preprint", "container_title": "arXiv", "updated_at": "2026-11-10" } ] }