Diagnosing Task Insensitivity in Language Agents

arXiv:2606.26918 · cs.AI · Submitted 2026-06-25 · Read on arXiv

Listen

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Diagnosing Task Insensitivity in Language Agents".

Jane: Here's the gist of the paper. The gist The model might continue with actions aligned with the original task even when the instruction is semantically corrupted and cannot be directly answered, which they term task insensitivity.

Tom: Evidence of Task-Level Overfitting To isolate whether strong benchmark performance reflect instruction following or memorization, we design a minimal diagnostic: we corrupt the task description while keeping the agent prompt otherwise intact.

Jane: The authors show that models often fail to recognize that an instruction is invalid and instead continue as if it were valid, even when explicitly told that they can ask for clarification <ref:2606.26918#pg6> In many such cases, the model effectively reconstructs the original task from the corrupted input <ref:2606.26918#pg4> This behavior reveals a specific failure mode: when the prompt resembles a familiar task, the model defaults to a learned policy even though the instruction no longer cleanly supports that behavior <ref:2606.26918#pg5> Task Reconstruction We evaluate only cases in which the generated action matches an action from the original task.

Tom: The authors demonstrate that corrupted tasks frequently elicit responses consistent with the original intent <ref:2606.26918#pg4> Taken together, these results indicate that when faced with an ambiguous instruction, models default to familiar policies rather than reasoning from the provided text <ref:2606.26918#pg4>.

Title and authors: Tom: So, this paper is called "Diagnosing Task Insensitivity in Language Agents", and it tackles this idea that language models might keep doing the same thing even when you change the instruction slightly. It’s about how these agents fail to generalize when things get a little different.

Jane: That sounds like something every developer worries about, right? Because if an AI can't tell the difference between two similar tasks, it means it's not really understanding what you actually asked for.

Lu: Exactly. The authors point out that this happens because models often reuse action patterns they learned during training even when the new task requires different behavior, like using a microwave for cooling bread instead of heating it up > Page two -- This paper Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Meng: So, what’s the core finding here? Is it just that they fail sometimes, or is there a systematic way this happens across different tasks? I need to know if this is a rare glitch or something we can control.

Tom: The paper shows that models often continue with actions that match the original task even when the instruction gets corrupted, and they actually do this even when you tell them they should ask for clarification > Page one -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,the original task even when the instruction>

Jane: Wait, so it’s not just that the model gets confused; it’s actively choosing an action that fits the old task, even when the new instruction is bad. That's a big distinction.

Lu: And they found that this behavior is accompanied by a consistent training-time attention drift, where the attention shifts away from the main task instructions and towards just looking at what’s happening in the current observation > Page two -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,this behavior is accompanied by a consistent training-time attention drift>

Meng: Attention drifting away from the task instruction sounds like a sign that they're relying too much on their immediate context, which is exactly what I worry about when we try to deploy things at scale.

Tom: Right. They found this pattern is consistent, suggesting it’s not just random noise but a specific policy developing during training where the model starts predicting actions from the local context instead of paying attention to the task itself > Page two -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Jane: So they are essentially defaulting to a learned shortcut because that's what they see most often when tasks are similar but distinct.

Lu: That’s the gap they want to address, which leads into their proposed solution, because standard next-action supervision doesn't give them enough pressure to keep that dependence on the task instruction > Page two -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

The paper's summary: Tom: Okay, so the paper summarizes this problem by looking at several different agent settings—ALFWorld, ScienceWorld, and WebShop—and they show concrete numbers about how often the model hits the original task versus reconstructing it from scratch when things are corrupted > Page three -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Jane: And those numbers are really telling. They show that for instance, in ALFWorld, when a task description is corrupted, the model has a zero point two five fraction of cases where it's consistent with the original task, but it’s still reconstructing the original intent about seventy-seven percent of the time > Page three -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Lu: What’s interesting is that even though it can reconstruct the original intent quite well, they find that models often still output an action consistent with the original task when you replace the task description with something different but similar > Page three -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3,an action consistent with the original>

Meng: So they’re not totally failing; they’re just being lazy and reusing a known pattern because it looks familiar, even when it shouldn't apply > Page three -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Tom: That’s the key idea: when faced with an ambiguous instruction, models default to familiar policies instead of actually reasoning through the new text they are given > Page three -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Jane: And this is what the authors call task reconstruction, and it suggests that their training methods aren't strong enough at making sure the model focuses on the instruction rather than just its own internal history > Page three -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Lu: Which means they’re suggesting that we need to change how we supervise these models so they are forced to maintain a stronger dependence on the task instruction, because right now, standard next-action supervision doesn't enforce that > Page two -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Meng: So if this is true, then the training process needs a mechanism to specifically penalize that kind of shortcut behavior when the task changes > Page two -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Tom: That leads directly into their proposed fix, which they call Task-Perturbed NLL Optimization, which they describe as a lightweight contrastive regularizer designed to encourage the original action to become less likely when the task description is replaced with a different one > Page two -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Jane: So it’s essentially adding a new loss term that forces the model to be more sensitive to changes in the task prompt, which is interesting because it’s a direct fix for what they identified as task insensitivity > Page two -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Lu: And this regularization aims to make sure that if the task instruction changes, the original response becomes less likely under the new instruction, which is their core goal > Page two -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

The paper's improvements: Tom: So what’s the proposed improvement? They introduce Task-Perturbed NLL Optimization as a lightweight contrastive regularizer that explicitly encourages action dependence on the task instruction > Page six -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Jane: How does this fix the problem of task insensitivity? It sounds like they're trying to make the original response less likely when the task changes, which is what you need for better out-of-distribution generalization.

Lu: The core idea is simple: if the task changes, the original response should become less likely > Page four -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Meng: So they are basically saying we can use this regularization to prevent models from relying on brittle heuristics inherited from training distribution when they encounter a task that’s slightly outside of what they've seen before > Page six -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Tom: It specifically encourages action dependence on the task instruction, and their main result shows that this method improves out-of-distribution performance in most settings, especially when local state is highly similar across tasks but the correct action really depends critically on the task instruction > Page four -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Jane: That sounds like it helps models when they have to make decisions where the task context is crucial and can't be recovered just from their immediate observation or history > Page six -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Lu: The way it works is that they define a reference ratio, rho(i) ref = NLL(i) perturb / NLL(i) vanilla + epsilon to measure how much less compatible the original response becomes after the task is replaced according to the reference model > Page six -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Meng: So they are measuring this compatibility using this ratio, and if that ratio is low, they know it’s a bad sign for generalization > Page four -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Tom: And the final training objective is just the standard supervised fine-tuning loss plus this regularization term, Ltotal = LSFT + lambda Lfloor > Page four -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Jane: So it’s not a massive overhaul; it’s adding a small piece of math that makes sure the model pays attention to the task instruction instead of just guessing based on familiarity > Page six -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Conclusion: Tom: To wrap up, this paper shows that task insensitivity is a concrete source of out-of-distribution failure in language agents > Page one -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Jane: So the main message for us is that we need to make sure models are explicitly penalized for reusing old patterns when the task changes, and this forces them to be more robust when they encounter new situations > Page one -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Lu: I think it points toward a direction where preserving action-level dependence on task instructions is a promising way to improve agent robustness beyond just standard next-action training > Page four -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Meng: From an engineering standpoint, this means we need to focus on making sure the system doesn't just rely on familiar behaviors when it encounters something new, which is what breaks things at scale > Page six -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg3>

Lalam: I’m pretty sure this method is a solid direction for making those agents more stable and less prone to task insensitivity > Page four -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Tom: Exactly. So we're looking at ways to make sure that when the task instruction changes, the original action becomes less likely under that new instruction > Page two -- Diagnosing Task Insensitivity in Language Agents <ref:2606.26918#pg1,Diagnosing Task Insensitivity in Language Agents>

Jane: It’s a solid direction, and it’s something worth paying attention to as we build these more capable agents.

Jingyu Liu, Xiaopeng Wu, Kehan Chen, Chuan Yu, ‘Yong Liu…

Gaoling School of Artificial Intelligence Renmin University of China · Taobao & Tmall group of Alibaba · Beijing Key Laboratory of Research on Large Models and Intelligent Governance · Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE

cs.AI

Submitted: 2026-06-25

Updated: 2026-10-19

Comments: 24 pages

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 82/100

The gist: The gist The model might continue with actions aligned with the original task even when the instruction is semantically corrupted and cannot be directly answered, which they term task insensitivity.

Key concepts

Task Insensitivity: This is a failure mode where an AI agent ignores a corrupted instruction and keeps performing actions consistent with its training. Instead of understanding the new request, it defaults to familiar behaviors because the prompt still looks like a recognizable task, leading to poor performance on unseen tasks.
Task Reconstruction: When faced with ambiguous or corrupted instructions, models often reconstruct the original task from the corrupted input. This happens because they rely on learned policies rather than carefully reasoning about the provided text, causing them to generate responses consistent with the original intent even when it's no longer explicitly stated.
Task-Perturbed NLL Optimization: This is a training technique that adds a mathematical penalty to the model's loss. It specifically encourages the model to change its predicted action when the task instruction changes. This forces the agent to rely more on the current task context rather than relying on old, brittle patterns learned during standard training.

Terminology

Generalization — 2× in this paper · explained in 14 episodes

Summary

The gist The model might continue with actions aligned with the original task even when the instruction is semantically corrupted and cannot be directly answered, which they term task insensitivity.

Evidence of Task-Level Overfitting

To isolate whether strong benchmark performance reflect instruction following or memorization, we design a minimal diagnostic: we corrupt the task description while keeping the agent prompt otherwise intact.

The authors show that models often fail to recognize that an instruction is invalid and instead continue as if it were valid, even when explicitly told that they can ask for clarification <ref:2606.26918#pg6> In many such cases, the model effectively reconstructs the original task from the corrupted input <ref:2606.26918#pg4> This behavior reveals a specific failure mode: when the prompt resembles a familiar task, the model defaults to a learned policy even though the instruction no longer cleanly supports that behavior <ref:2606.26918#pg5>

Task Reconstruction

We evaluate only cases in which the generated action matches an action from the original task.

The authors demonstrate that corrupted tasks frequently elicit responses consistent with the original intent <ref:2606.26918#pg4> Taken together, these results indicate that when faced with an ambiguous instruction, models default to familiar policies rather than reasoning from the provided text <ref:2606.26918#pg4>.

Training-Time Dynamics Associated with Overfitting

We train agents via supervised fine-tuning and reinforcement learning, and track diagnostic metrics across training checkpoints.

The probability that the model’s predicted action under a corrupted task description remains aligned with the original task increases over training, which suggests that the model becomes increasingly likely to recover familiar task patterns from incomplete or ambiguous instructions <ref:2606.26918#pg6> In addition, the training responses always restate the task description at the beginning of the response <ref:2606.26918#pg4>.

Observed Attention Drift During Training

We decompose the agent prompt into five regions: Overall task, Response style, Current observation, available actions and Others.

During training, attention to the overall task region decreases, while attention to the current observation region increases <ref:2606.26918#pg4>. This observed pattern suggests a relative reweighting rather than a complete collapse of task use <ref:2606.26918#pg4>. The analysis suggests that during training, the model may gradually rely more on current observations and recent history for next-action prediction, which is consistent with the empirically observed decline in relative attention to task tokens <ref:2606.26918#pg6>.

Method: Task-Perturbed NLL Optimization

We augment standard supervised fine-tuning (SFT) with a task-perturbation regularizer.

The authors propose Task-Perturbed NLL Optimization, a lightweight contrastive regularizer that explicitly encourages action dependence on the task instruction <ref:2606.26918#pg6>. The core desideratum is simple: if the task changes, the original response should become less likely <ref:2606.26918#pg4>. They define a reference ratio ρ(i) ref = NLL(i) perturb / NLL(i) vanilla + ϵ, which measures how much less compatible the original response becomes after the task is replaced, according to the reference model <ref:2606.26918#pg6>. The final training objective is then Ltotal = LSFT + λLfloor, where LSFT is the standard supervised fine-tuning loss and λ is the regularization weight <ref:2606.26918#pg4>.

Main Results

Our method improves OOD performance in most settings.

The results show that our method improves OOD performance in the controlled task-split setting <ref:2606.26918#pg6>. The gains are most consistent under GRPO and in task pairs where local state is highly similar across tasks but the correct action depends critically on the task instruction <ref:2606.26918#pg4>. Overall, the results suggest that explicit task sensitivity regularization can improve OOD generalization in these controlled settings when the task instruction provides information that cannot be recovered reliably from state and history alone <ref:2606.26918#pg6>.

Conclusion

We identify task insensitivity as a concrete and measurable source of OOD failure in language agents.

Taken together, these results suggest that preserving action-level dependence on task instructions is a promising direction for improving agent robustness beyond standard next-action training <ref:2606.26918#pg4>. The authors conclude that explicit tasksensitivity regularization can improve OOD generalization in the controlled settings they study <ref:2606.26918#pg6>.

Limitations

Our empirical study covers only three agent settings: ALFWorld, ScienceWorld, and WebShop.

The authors note that their empirical study covers only three agent settings: ALFWorld, ScienceWorld, and WebShop <ref:2606.26918#pg4>. Their attention analysis is mechanistically suggestive rather than causal <ref:2606.26918#pg4>. The toy view does not claim that transformer attention directly equals feature importance, nor that task attention must literally collapse.

References

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat and 1 others. 2023. GPT-4 technical report. arXiv preprint arXiv:2303.08774.

Hao Bai, Yifei Zhou, Jiayi Pan, Mert Cemri, Alane Suhr, Sergey Levine, and Aviral Kumar. 2024. Digirl: Training in-the-wild device-control agents with autonomous reinforcement learning. Advances in Neural Information Processing Systems, 37:12461–12495.

Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang and 1 others. 2023. Qwen technical report. arXiv preprint arXiv:2309.16609.

Kevin Chen, Marco Cusumano-Towner, Brody Huval, Aleksei Petrenko, Jackson Hamburger, Vladlen Koltun, and Philipp Krähenbühl. 2025. Reinforcement learning for long-horizon interactive llm agents. arXiv preprint arXiv:2502.01600.

Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc V Le, Sergey Levine, and Yi Ma. 2025. SFT memorizes, RL generalizes: A comparative study of foundation model post-training. arXiv preprint arXiv:2501.17161.

Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11):665–673.

Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang and 1 others. 2025. DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv preprint arXiv:2501.12948.

Divyansh Kaushik, Eduard Hovy, and Zachary C. Lipton. 2020. Learning the difference that makes a difference with counterfactually-augmented data. In International Conference on Learning Representations.

Jingyu Liu, Xiaopeng Wu, Jingquan Peng, Kehan Chen, Chuan Yu, Lizhong Ding, and Yong Liu. 2025. Gradient coupling: The hidden barrier to generalization in agentic reinforcement learning. arXiv preprint arXiv:2509.23870.

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray and 1 others. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730–27744.

**Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu and 1 others. 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300.

Improvements for AI systems

Task-Perturbed NLL Optimization can be used to train models such that the original action should become less likely when the task changes. This improves OOD generalization by explicitly encouraging action dependence on the task instruction and preserving more stable attention to task tokens.
The improved agent can better handle out-of-distribution (OOD) tasks by resisting the tendency to reuse action patterns learned during training even when the new task requires different behavior. This is achieved because the regularization prevents models from relying on brittle heuristics inherited from the training distribution.
Models will exhibit more stable attention during inference, as shown by Figure 9, where attention to the task description is better preserved over training under Task-Perturbed NLL Optimization. This leads to a reduction in conditional consistency, indicating that taskinsensitive behavior grows more slowly.