Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuning

TL;DR

This paper identifies endogenous reasoning drift in multi-modal large language models and introduces CPO++, a framework that enhances robustness and generalization in dynamic, safety-critical domains.

Contribution

It defines endogenous reasoning drift as multi-modal concept drift and proposes CPO++, integrating counterfactual reasoning and preference optimization for adaptive robustness.

Findings

CPO++ improves reasoning coherence and decision accuracy.

Framework demonstrates robustness against extreme interference.

Exhibits strong zero-shot cross-domain generalization.

Abstract

Reinforcement Fine-Tuning (RFT) has established itself as a critical paradigm for the alignment of Multi-modal Large Language Models (MLLMs) with complex human values and domain-specific requirements. Nevertheless, current research primarily focuses on mitigating exogenous distribution shifts arising from data-centric factors, the non-stationarity inherent in the endogenous reasoning remains largely unexplored. In this work, a critical vulnerability is revealed within MLLMs: they are highly susceptible to endogenous reasoning drift, across both thinking and perception perspectives. It manifests as unpredictable distribution changes that emerge spontaneously during the autoregressive generation process, independent of external environmental perturbations. To adapt it, we first theoretically define endogenous reasoning drift within the RFT of MLLMs as the multi-modal concept drift. In this context, this paper proposes Counterfactual Preference Optimization ++ (CPO++), a comprehensive and autonomous framework adapted to the multi-modal concept drift. It integrates counterfactual reasoning with domain knowledge to execute controlled perturbations across thinking and perception, employing preference optimization to disentangle spurious correlations. Extensive empirical evaluations across two highly dynamic and safety-critical domains: medical diagnosis and autonomous driving. They demonstrate that the proposed framework achieves superior performance in reasoning coherence, decision-making precision, and inherent robustness against extreme interference. The methodology also exhibits exceptional zero-shot cross-domain generalization, providing a principled foundation for reliable multi-modal reasoning in safety-critical applications.