SEIF: Self-Evolving Reinforcement Learning for Instruction Following
Summary
This paper introduces SEIF, a self-evolving reinforcement learning framework that enhances LLM instruction-following capabilities through iterative difficulty adaptation and co-training of instructor and follower components.
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Paper page - SEIF: Self-Evolving Reinforcement Learning for Instruction Following
Source: https://huggingface.co/papers/2605.07465 Published on May 8
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Submitted byhttps://huggingface.co/dd12345789
rainon May 12
Abstract
A self-evolving reinforcement learning framework enhances large language model instruction-following capabilities through iterative difficulty adaptation and co-training of instructor and follower components.
Instruction following is a fundamental capability oflarge language models(LLMs), yet continuously improving this capability remains challenging. Existing methods typically rely either on costly external supervision from humans or strong teacher models, or on self-play training with static-difficulty instructions that cannot evolve as the model’s capabilities improve. To address these limitations, we propose SEIF (Self-EvolvingReinforcement Learningfor Instruction Following), a self-evolving framework for enhancing theinstruction-followingability of LLMs. SEIF forms a closedself-evolutionloop that improves the model’sinstruction-followingability, whereinstruction difficulty evolutionandmodel capability evolutionreinforce each other. SEIF consists of four roles: an Instructor that generates increasingly challenging instructions, a Filter that removes conflicting or invalid instructions to ensure data quality, a Follower that learns to follow evolved instructions, and a Judger that providesreward signalsforreinforcement learning. The Instructor and Follower are alternately trained and co-evolve throughout the process. Experiments across multiple model scales and architectures show that SEIF consistently improvesinstruction-followingperformance, suggesting strong generality. Further analyses reveal the sources of improvement and identify an effective training strategy forself-evolutionon open-ended tasks: sufficient early-stage training to build a solid foundation, followed by moderate late-stage training to mitigate overfitting and achieve better final performance. The code and data are publicly available at https://github.com/Rainier-rq1/SEIF.
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