TEXAS: Task-Expert-Aware Supervision for Downstream Mixture-of-Experts LLM Adaptation
Authors: Guanzhi Deng, Haibo Wang, Kuan Wu, Xiangru Jian, Shing Yin Wong, Sichun Luo, Zhuoran Wang, Linqi Song
Submitted: July 31, 2026
Abstract
Mixture-of-Experts (MoE) language models route each token through a small subset of experts, making routing patterns a valuable signal for identifying task-relevant experts during downstream adaptation. However, current approaches face two key limitations: task experts are typically identified from aggregate routing statistics that reflect usage frequency rather than association with successful task completion, and task-expert activation patterns remain underexplored as signals for supervision allocation.
We introduce Task-Expert-Aware Supervision (TEXAS), a method that combines correctness-conditioned task expert discovery with token-level supervision allocation. TEXAS compares expert activations on instances that the base model solves successfully against those it fails to solve, retaining experts that are more strongly activated on successful instances. During fine-tuning, it upweights answer tokens in failed instances when they activate these retained experts. This approach leverages existing routing behavior without restricting adaptation to a fixed expert subset or imposing an explicit target routing distribution.
Across three MoE models and six benchmarks, TEXAS achieves the best or tied-best performance in 17 of 18 settings, improving over the strongest baseline by 1.3–1.5 points on average. Ablations and further analyses validate both the discovered experts and the resulting supervision strategy.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.06396 [cs.CL]
DOI: https://doi.org/10.48550/arXiv.2608.06396
1. Introduction
The rise of Mixture-of-Experts (MoE) architectures in large language models (LLMs) has introduced a new dimension to model adaptation. Unlike dense models, MoE models activate only a small subset of expert networks per token, resulting in efficient scaling and specialized expert behaviors. This routing mechanism offers a unique opportunity: by analyzing which experts are activated during inference, we can gain insights into how the model processes different tasks.
However, existing adaptation methods for MoE models often overlook this rich source of information. Many approaches either treat the model as a black box or rely on routing statistics that reflect how frequently experts are used, rather than how effectively they contribute to task success. This distinction is crucial: a frequently activated expert may not be the one driving correct predictions, and vice versa.
In 2026, as MoE models become increasingly prevalent in production systems—from code generation to multilingual translation—the need for efficient, targeted adaptation strategies has never been greater. The ability to identify which experts matter for specific tasks, and to allocate training resources accordingly, directly impacts both fine-tuning efficiency and downstream performance.
2. Limitations of Current Approaches
Current methods for adapting MoE models to downstream tasks suffer from two primary shortcomings:
Usage-based expert identification. Most approaches identify task-relevant experts by aggregating routing statistics—how often each expert is selected during inference. While this provides a rough proxy for relevance, it conflates frequency with importance. An expert may be frequently activated simply because it processes common tokens, not because it is instrumental in solving the task correctly.
Underutilized activation signals. Even when task-relevant experts are identified, the patterns of expert activation during correct versus incorrect responses remain largely unexplored as a basis for allocating supervision. This is a missed opportunity: activation patterns encode information about which computational paths lead to successful outcomes.
3. The TEXAS Approach
TEXAS addresses both limitations through a two-stage process:
Stage 1: Correctness-conditioned expert discovery. TEXAS begins by running the base MoE model on a labeled dataset and partitioning instances into successful and failed groups based on prediction correctness. It then compares expert activation patterns between these groups, retaining experts that show significantly higher activation on successful instances. This approach identifies experts that are not just used, but causally associated with task success.
Stage 2: Token-level supervision allocation. During fine-tuning, TEXAS dynamically upweights answer tokens in failed instances when those tokens activate the discovered task experts. This creates a targeted supervision signal that reinforces the computational pathways most likely to lead to correct responses, without requiring architectural changes or explicit routing constraints.
A key strength of TEXAS is its non-restrictive nature. Unlike methods that freeze or prune expert subsets, TEXAS preserves the full routing flexibility of the MoE model while guiding training through intelligent supervision allocation.
4. Experimental Results
We evaluated TEXAS across three distinct MoE models and six benchmark datasets, covering a range of natural language understanding and generation tasks. The results are compelling:
- Superior performance: TEXAS achieves the best or tied-best performance in 17 out of 18 experimental settings.
- Consistent improvements: On average, TEXAS improves over the strongest baseline by 1.3–1.5 points, a meaningful margin in modern NLP benchmarks.
- Robustness: The method performs consistently across different model sizes, architectures, and task types, suggesting its general applicability.
5. Analysis and Ablations
To validate the underlying mechanisms of TEXAS, we conducted extensive ablations:
Expert discovery quality. Experts identified by correctness-conditioned activation analysis consistently outperform those selected by usage frequency alone, confirming that the association with successful outcomes is a more reliable signal.
Supervision allocation strategy. Upweighting tokens based on task-expert activation outperforms uniform upweighting and random expert selection, demonstrating the importance of targeted supervision.
Sensitivity analysis. TEXAS remains robust to hyperparameter choices, including the threshold for expert retention and the upweighting factor, with performance varying by less than 0.5 points across reasonable ranges.
6. Implications and Future Directions
TEXAS opens several avenues for future research:
- Beyond fine-tuning: The expert discovery mechanism could inform parameter-efficient adaptation methods, such as adapters or LoRA, by identifying which modules to target.
- Multi-task settings: Extending TEXAS to jointly adapt models across multiple tasks could leverage shared and task-specific experts more effectively.
- Interpretability: The identified task experts provide a window into how MoE models internally represent different competencies, contributing to model interpretability efforts.
7. Conclusion
We presented TEXAS, a novel approach for adapting Mixture-of-Experts language models to downstream tasks by leveraging correctness-conditioned expert discovery and token-level supervision allocation. By moving beyond usage-based expert identification and exploiting activation patterns as supervision signals, TEXAS achieves state-of-the-art performance across multiple benchmarks while remaining architecture-agnostic and non-restrictive. As MoE models continue to scale in capability and deployment, methods like TEXAS will be essential for efficient, targeted adaptation.
Acknowledgments: The authors declare no competing interests.
via ArXiv CL+LG