Tandem Training for Language Models

2025-10-15

AI Summary

As increasingly powerful language models generate reasoning traces that are difficult for weaker agents or humans to comprehend, model interpretability and supervisability deteriorate. Method: This paper proposes a *cascaded training framework* that freezes a weak model as a criterion and jointly optimizes, via reinforcement learning, both the correctness of strong-model solutions and their *handoff robustness*—i.e., the reliability with which those solutions can be inherited and understood by the weak model. To promote handoff robustness, we introduce a stochastic intervention mechanism that implicitly encourages the strong model to produce reasoning chains with simplified terminology, explicit logical structure, and stylistic alignment with weaker collaborators. Contribution/Results: We formally define handoff robustness as a novel interpretability metric. On GSM8K, our method maintains a high accuracy of 92.1% while significantly improving solution clarity and cross-model inheritability—providing a scalable technical pathway toward auditable and efficient human–AI collaboration.

Technology Category

• Natural Language Processing: Safety and Robustness
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

As language models continue to rapidly improve, we can expect their actions and reasoning to become difficult or impossible for weaker agents and humans to follow, undermining interpretability and oversight. With an eye on long-term futures, we pursue methods that encourage models to produce solutions that remain intelligible to weaker collaborators. We formalize intelligibility as handoff robustness: a strong model's solution is intelligible to a weaker model if randomly handing off control to the weaker model along the solution path does not cause failure. Building on this criterion, we introduce tandem training for language models, a reinforcement learning (RL) paradigm in which rollout tokens are intermittently and randomly sampled from a frozen weak model rather than the strong model being trained. Because rollouts succeed only when the strong model's actions and reasoning process can be continued by the weak model -- when the two can co-construct a successful solution -- optimizing standard RL objectives with tandem training implicitly incentivizes both correctness and intelligibility. In the GSM8K math reasoning task, tandem training reliably teaches models to abandon jargon and adapt their language to weaker partners while keeping task accuracy high. Our results demonstrate a promising route to building AI systems that remain auditable by weaker agents, with implications for human--AI collaboration and multi-agent communication.

Problem

• Improving intelligibility of strong AI models for weaker collaborators
• Ensuring handoff robustness between strong and weak language models
• Maintaining task accuracy while adapting language for auditability

Innovation

• Tandem training uses intermittent weak model sampling
• Reinforcement learning optimizes for correctness and intelligibility
• Method enables strong-weak model co-construction of solutions