dorsal/arxiv
View SchemaLSRIF: Logic-Structured Reinforcement Learning for Instruction Following
| Authors | Qingyu Ren, Qianyu He, Jingwen Chang, Jie Zeng, Jiaqing Liang, Yanghua Xiao, Han Xia, Zeye Sun, Fei Yu |
|---|---|
| Categories | |
| ArXiv ID | 2601.06431vv1 |
| URL | https://arxiv.org/abs/2601.06431 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Instruction-following is critical for large language models, but real-world instructions often contain logical structures such as sequential dependencies and conditional branching. Existing methods typically construct datasets with parallel constraints and optimize average rewards, ignoring logical dependencies and yielding noisy signals. We propose a logic-structured training framework LSRIF that explicitly models instruction logic. We first construct a dataset LSRInstruct with constraint structures such as parallel, sequential, and conditional types, and then design structure-aware rewarding method LSRIF including average aggregation for parallel structures, failure-penalty propagation for sequential structures, and selective rewards for conditional branches. Experiments show LSRIF brings significant improvements in instruction-following (in-domain and out-of-domain) and general reasoning. Analysis reveals that learning with explicit logic structures brings parameter updates in attention layers and sharpens token-level attention to constraints and logical operators.
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"date_created": "2026-02-17T05:53:08.228000Z",
"date_modified": "2026-02-17T05:53:08.228000Z",
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"abstract": "Instruction-following is critical for large language models, but real-world instructions often contain logical structures such as sequential dependencies and conditional branching. Existing methods typically construct datasets with parallel constraints and optimize average rewards, ignoring logical dependencies and yielding noisy signals. We propose a logic-structured training framework LSRIF that explicitly models instruction logic. We first construct a dataset LSRInstruct with constraint structures such as parallel, sequential, and conditional types, and then design structure-aware rewarding method LSRIF including average aggregation for parallel structures, failure-penalty propagation for sequential structures, and selective rewards for conditional branches. Experiments show LSRIF brings significant improvements in instruction-following (in-domain and out-of-domain) and general reasoning. Analysis reveals that learning with explicit logic structures brings parameter updates in attention layers and sharpens token-level attention to constraints and logical operators.",
"arxiv_id": "2601.06431",
"authors": [
"Qingyu Ren",
"Qianyu He",
"Jingwen Chang",
"Jie Zeng",
"Jiaqing Liang",
"Yanghua Xiao",
"Han Xia",
"Zeye Sun",
"Fei Yu"
],
"categories": [
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "LSRIF: Logic-Structured Reinforcement Learning for Instruction Following",
"url": "https://arxiv.org/abs/2601.06431",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "df325ed8-6c14-41f8-b6ba-c287420440ac",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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