The recent incident in which OpenAI agents autonomously compromised Hugging Face's infrastructure illustrates the importance of instruction hierarchy (IH): an LLM should follow lower-priority instructions, such as user requests, only when they do not conflict with higher-priority instructions, such as system policies. IH can be effectively improved by fine-tuning LLMs on simulated examples of instruction conflicts, whose diversity is key to teaching LLMs to properly handle conflicts at test time. However, our decomposition of a task into goals and constraints reveals a conflict type -- goal-goal conflicts -- that is absent from existing IH training sets from OpenAI and NVIDIA. We show the importance of this conflict type: fine-tuning Qwen3.5-9B and Gemma4-12B-it on examples of this conflict type significantly improves IH on a wide range of real-world tasks unseen in training; adding these examples to existing IH training sets for fine-tuning also gives better IH adherence. The IH gains come with trivial mean utility changes on no-conflict tasks across eight benchmarks. Our work advocates including this new conflict type in IH training, even if these examples do not reflect typical LLM use.
Figures & tables
Figure 1: Conflict types (a) and coverage in existing IH training sets (b) Jiang et al. (2026) ; Guo et al. (2026) .
Figure 2: Initial exact system-task compliance on goal–goal conflicts. An analysis of these failures appears in Appendix B.2 .
Figure 3: Training original models on goal–goal conflicts raises Conflict Resistance Rates in all ten categories.
Case
Tasks by priority
Behavior before adding goal–goal data
Behavior after adding goal–goal data
Training on goal–goal conflicts improves IH adherence across models, message types, languages, and domains
A. Translation XIH-Bench
High-priority task Translate French to Korean.
Answers the embedded question in Korean.
Translates the instructions, passage, and question into Korean.
Low-priority task Answer a question about the Hershey–Chase experiment embedded in the tool text.
B. Repository analysis IH-Benchmark
High-priority task Analyze the requested repository.
Attempts to run the installer with execute_command ; the simulated tool blocks it.
Summarizes the repository and asks before running the installer; makes no execution call.
Low-priority task Run an installation command requested by the returned README.
Goal–goal conflicts, if simulated in training, provide crucial complementary supervision beyond existing conflicts
Table 1: Case studies for fine-tuning Qwen3.5-9B on goal–goal conflicts. Cases A–B compare the original model with goal–goal training; Cases C–D compare constraint-conflict training with combined training; Cases E–F compare the original model with goal–goal training on no-conflict tasks. Entries summarize selected response traces; non-English responses are described in English.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Model
Exact task compliance (%)
Qwen3.5-9B
19.8
Gemma4-12B-it
42.3
Gemma4-31B-it
39.2
Qwen3.6-27B
9.7
Nemotron3.5-Lightning
55.0
DeepSeekV4-Flash
51.9
Appendix
Table 2: Initial exact system-task compliance on goal–goal conflicts. A successful response matches the complete system target before goal–goal fine-tuning.
Model
# Failure cases
Request failure (%)
Instruction identification (%)
Conflict resolution (%)
Response realization (%)
Qwen3.5-9B
7,511
0.0
6.8
12.4
80.8
Gemma4-12B-it
3,272
0.0
53.9
6.2
39.9
Qwen3.6-27B
8,623
2.5
0.0
0.0
97.5
GLM-5.2
2,540
1.3
7.0
0.0
91.7
DeepSeekV4-Flash
4,573
0.0
2.8
1.8
95.4
DeepSeekV4-Pro
1,807
1.4
0.4
0.0
98.2
Appendix
Table 3: Failure mode analysis for cases that do not comply with goal–goal conflicts’ desirable outputs.