TRIDENT is a self-improving reasoning framework that treats reasoning as a graph search process rather than a single chain of thought. It explores multiple reasoning paths using Tree-of-Thoughts and represents them as a structured thought graph. A Graph Neural Network encodes this graph and predicts a promise score to guide which reasoning branches to expand or prune. This learned verification is more reliable than raw generation, making path selection efficient and accurate. A multi-agent policy balances exploration, backtracking, and self-reflection to avoid dead ends and mode collapse. Adaptive early stopping reduces compute by halting search when reasoning paths converge or reach high confidence. TRIDENT generates its own training data by identifying high-variance problems where the model is inconsistent. Successful reasoning traces are distilled without human annotation. The model is improved via LoRA-based fine-tuning with stabilized rewards. Overall, TRIDENT shows that algorithmic search can significantly amplify the reasoning ability of small language models.
Leveraging state-of-the-art machine learning and neural network technologies
Multi-agent frameworks enabling complex problem-solving and optimization
Real-world applications and deployment strategies for various domains
Optimized for high-performance computing and large-scale applications