Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning
Summary: arXiv:2604.20601v1 Announce Type: new
Abstract: We introduce SuperIgor, a framework for instruction-following tasks. Unlike prior methods that rely on predefined subtasks, SuperIgor enables a language model to generate and refine high-level plans through a self-learning mechanism, reducing the need for manual dataset annotation. Our approach involves iterative co-training: an RL agent is trained to follow the generated plans, while the language model adapts and modifies these plans based on RL feedback and preferences. This creates a feedback loop where both the agent and the planner improve jointly. We validate our framework in environments with rich dynamics and stochasticity. Results show that SuperIgor agents adhere to instructions more strictly than baseline methods, while also demonstrating strong generalization to previously unseen instructions.
Introduction
The field of artificial intelligence has witnessed significant advancements in instruction-following tasks, primarily through the development of frameworks that allow for more dynamic and adaptable responses. Traditional methods often rely on a fixed set of subtasks, which can limit flexibility and adaptability. However, the introduction of SuperIgor marks a transformative step in this domain.
Overview of SuperIgor
SuperIgor is designed to enhance the interaction between a language model and a reinforcement learning (RL) agent. This framework enables the generation and refinement of plans without the constraints of predefined subtasks. The self-learning mechanism employed in SuperIgor allows for continuous improvement and adaptation, significantly reducing the reliance on manually annotated datasets.
Key Features
- Self-Learning Mechanism: SuperIgor empowers language models to generate high-level plans autonomously, which can be iteratively refined based on feedback from the RL agent.
- Iterative Co-Training: The framework facilitates a symbiotic relationship between the RL agent and the language model, where both components learn from each other.
- Adaptability: SuperIgor is validated in complex environments characterized by rich dynamics and stochasticity, showcasing its robust performance in unpredictable scenarios.
- Generalization Capabilities: The results indicate that SuperIgor agents can generalize effectively to new and unseen instructions, which is a critical aspect of AI development.
Results and Validation
In extensive testing, SuperIgor agents demonstrated a marked improvement in adherence to instructions compared to baseline methods. The joint learning process not only enhanced the agents’ performance but also allowed them to tackle instructions that had not been previously encountered. The ability to adapt and refine plans in real-time is a significant leap forward in the realm of AI instruction-following capabilities.
Conclusion
SuperIgor represents a novel approach to instruction-following tasks, leveraging the power of self-guided plan extraction and goal-conditional reinforcement learning. By eliminating the need for predefined subtasks and reducing manual annotation, SuperIgor sets a new standard in the development of flexible and adaptive AI systems. As AI continues to evolve, frameworks like SuperIgor will be crucial in enhancing the effectiveness and applicability of instruction-following technologies across various sectors.