STAR: Similarity-guided Teacher-Assisted Refinement for Super-Tiny Function Calling Models

The proliferation of Large Language Models (LLMs) in function calling is pivotal for creating advanced AI agents, yet their large scale hinders widespread adoption, necessitating transferring their capabilities into smaller ones. However, existing paradigms are often plagued by overfitting, training instability, ineffective binary rewards for multi-solution tasks, and the difficulty of synergizing techniques. We introduce STAR: Similarity-guided Teacher-Assisted Refinement, a novel holistic framework that effectively transfers LLMs' capabilities to super-tiny models. STAR consists of two core technical innovations: (1) Constrained Knowledge Distillation (CKD), a training objective that augments top-k forward KL divergence to suppress confidently incorrect predictions, ensuring training stability while preserving exploration capacity for downstream RL. STAR holistically synergizes these strategies within a cohesive training curriculum, enabling super-tiny models to achieve exceptional performance on complex function calling tasks; (2) Similarity-guided RL (Sim-RL), a RL mechanism that introduces a fine-grained, similarity-based reward. This provides a robust, continuous, and rich signal for better policy optimization by evaluating the similarity between generated outputs and the ground truth. Extensive experiments on challenging and renowned benchmarks demonstrate the effectiveness of our method. Our STAR models establish SOTA in their size classes, significantly outperforming baselines. Remarkably, our 0.6B STAR model achieves the best performance among all open models under 1B, surpassing even several well-known open models at a larger scale. STAR demonstrates a training framework that distills capabilities of LLMs into super-tiny models, paving the way for powerful, accessible, and efficient AI agents.

From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs

In recent years, Large Language Models (LLMs) have significantly advanced artificial intelligence by optimizing traditional Natural Language Processing (NLP) pipelines, improving performance and generalization. This has spurred their integration into various systems. Many NLP systems, including ours, employ a "one-stage" pipeline directly incorporating LLMs. While effective, this approach incurs substantial costs and latency due to the need for large model parameters to achieve satisfactory outcomes. This paper introduces a three-stage cost-efficient end-to-end LLM deployment pipeline-including prototyping, knowledge transfer, and model compression-to tackle the cost-performance dilemma in LLM-based frameworks. Our approach yields a super tiny model optimized for cost and performance in online systems, simplifying the system architecture. Initially, by transforming complex tasks into a function call-based LLM-driven pipeline, an optimal performance prototype system is constructed to produce high-quality data as a teacher model. The second stage combine techniques like rejection fine-tuning, reinforcement learning and knowledge distillation to transfer knowledge to a smaller 0.5B student model, delivering effective performance at minimal cost. The final stage applies quantization and pruning to extremely compress model to 0.4B, achieving ultra-low latency and cost. The framework's modular design and cross-domain capabilities suggest potential applicability in other NLP areas.

Network Pruning Spaces

Network pruning techniques, including weight pruning and filter pruning, reveal that most state-of-the-art neural networks can be accelerated without a significant performance drop. This work focuses on filter pruning which enables accelerated inference with any off-the-shelf deep learning library and hardware. We propose the concept of \emph{network pruning spaces} that parametrize populations of subnetwork architectures. Based on this concept, we explore the structure aspect of subnetworks that result in minimal loss of accuracy in different pruning regimes and arrive at a series of observations by comparing subnetwork distributions. We conjecture through empirical studies that there exists an optimal FLOPs-to-parameter-bucket ratio related to the design of original network in a pruning regime. Statistically, the structure of a winning subnetwork guarantees an approximately optimal ratio in this regime. Upon our conjectures, we further refine the initial pruning space to reduce the cost of searching a good subnetwork architecture. Our experimental results on ImageNet show that the subnetwork we found is superior to those from the state-of-the-art pruning methods under comparable FLOPs.