SEIF creates a self-reinforcing loop in which an LLM alternately generates increasingly difficult instructions and learns to follow them better using reinforcement learning signals from its own judgments.
Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following
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abstract
Language models often struggle to follow multi-constraint instructions that are crucial for real-world applications. Existing reinforcement learning (RL) approaches suffer from dependency on external supervision and sparse reward signals from multi-constraint tasks. We propose a label-free self-supervised RL framework that eliminates dependency on external supervision by deriving reward signals directly from instructions and generating pseudo-labels for reward model training. Our approach introduces constraint decomposition strategies and efficient constraint-wise binary classification to address sparse reward challenges while maintaining computational efficiency. Experiments show that our approach generalizes well, achieving strong improvements across 3 in-domain and 5 out-of-domain datasets, including challenging agentic and multi-turn instruction following. The data and code are publicly available at https://github.com/Rainier-rq/verl-if
fields
cs.CL 2years
2026 2representative citing papers
WEval and WRL introduce fine-grained benchmarking and requirement-selective sample construction for training writing reward models, yielding substantial gains on writing benchmarks with strong generalization.
citing papers explorer
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SEIF: Self-Evolving Reinforcement Learning for Instruction Following
SEIF creates a self-reinforcing loop in which an LLM alternately generates increasingly difficult instructions and learns to follow them better using reinforcement learning signals from its own judgments.
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From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks
WEval and WRL introduce fine-grained benchmarking and requirement-selective sample construction for training writing reward models, yielding substantial gains on writing benchmarks with strong generalization.