Collaborative Learning and Reasoning over Model Slices

Large language models (LLMs) excel across diverse tasks, benefiting from knowledge priors, in-context reasoning, and test-time computation. Despite these strengths, single-model reasoning remains vulnerable to error propagation, as later steps depend on the model’s earlier outputs, leaving reasoning blind spots difficult to independently reassess. To address this limitation, we develop $\mathit{\text{collaborative}}\mathit{\text{learning}}$, encompassing the construction of heterogeneous collaborators and the learning of effective cross-model collaboration. Specifically, $\mathit{\text{model}}\mathit{\text{slicing}}$ replaces a large model with smaller, heterogeneous models under a comparable parameter budget, enabling prior reasoning to be reassessed across distinct model parameterizations. $\mathit{\text{Pyramid}}\mathit{\text{supervision}}$ combines protocol distillation with step-level supervision to refine reasoning and interaction decisions throughout collaboration. We evaluate our approach across four task categories: multi-hop question answering, mathematical reasoning, code generation, and open-ended constrained generation. The trained heterogeneous models achieve comparable or better task performance than the original large model and established multi-agent baselines, including reinforcement-learning-based methods, while also achieving higher inference efficiency than the multi-agent baselines and exhibiting differentiated collaboration patterns across tasks. These findings show that learned collaboration turns model heterogeneity into complementary reasoning capabilities.