Title: Optimizing Diversity and Quality through Base–Aligned Model Collaboration
Primary area: foundation or frontier models, including LLMs
Keywords: Open-ended text generation, LLM diversity, Multi-model collaboration

Abstract: Alignment has greatly improved large language models (LLMs)’ generation quality at the cost of output diversity, yielding highly similar outputs across samplings. We propose BACo, an inference-time token-level model collaboration framework that dynamically combines a base LLM (for diversity) with its aligned counterpart (for quality), generalizing nudging (Fei et al., 2025) into a full bidirectional model collaboration to optimize and balance diversity and quality. BACo adopts routing strategies based on next-token prediction uncertainty and semantic role heuristics to determine which model to decode from at each step. Unlike prior approaches via retraining, prompt engineering, or multiple sampling passes, BACo can generate diverse yet high-quality outputs and offers strong controllability via a family of routing strategies. Across three open-ended generation tasks and 13 metrics covering diversity and quality, BACo consistently surpasses state-of-the-art baselines. With our best router, BACo jointly enhances quality and diversity by 21.3% across all tasks and metrics. Human evaluations mirror these gains, highlighting that BACo reliably produces outputs that are both varied and high-quality.