Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following
Summary: arXiv:2510.14420v4 Announce Type: replace-cross
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.
Introduction
The advent of language models has transformed how machines understand and execute instructions. However, despite significant advances, these models often struggle with complex, multi-constraint instructions that are essential in real-world scenarios. Traditional reinforcement learning methods rely heavily on external supervision, which can be both costly and time-consuming. Moreover, the sparse reward signals from multi-constraint tasks further complicate the learning process.
Proposed Methodology
In light of these challenges, we propose a novel self-supervised RL framework that eliminates the need for external supervision. Our approach is centered on deriving reward signals directly from the instructions given to the language model. By generating pseudo-labels for the reward model training, we can effectively guide the learning process without the reliance on external inputs.
Key Features
- Label-Free Learning: Our framework operates without external supervision, making it more efficient and scalable.
- Constraint Decomposition: We introduce strategies to decompose complex constraints into manageable components, simplifying the instruction-following process.
- Efficient Classification: By implementing constraint-wise binary classification, we address the challenge of sparse rewards while ensuring computational efficiency.
Results
The efficacy of our self-supervised RL framework is demonstrated through extensive experiments across multiple datasets. We evaluate our approach on three in-domain and five out-of-domain datasets, showcasing its ability to generalize well. Our results indicate significant improvements in handling agentic and multi-turn instruction following tasks, which are typically challenging for existing models.
Conclusion
Our proposed self-supervised reinforcement learning framework represents a significant advancement in the field of instruction following in language models. By eliminating dependency on external supervision and effectively addressing the sparsity of reward signals, we pave the way for more robust and adaptable AI systems. The data and code supporting our findings are publicly available at https://github.com/Rainier-rq/verl-if.