Papers by Qingyu Ren
Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models (2025.findings-acl)
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| Challenge: | In real-world scenarios, user instructions often contain soft constraints, which are semantically related and cannot be rule-based verified, posing challenges for large language models. |
| Approach: | They propose a pipeline to construct datasets with high-quality outputs for instructions containing soft constraints automatically and use Direct Preference Optimization (DPO) as the training method. |
| Outcome: | The proposed model improves the LLMs' soft constraint following ability by using direct preference optimization (DPO) and constraint quantity. |
From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks (2026.findings-acl)
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| Challenge: | Existing evaluation benchmarks for writing reward models are coarse-grained. |
| Approach: | They propose a benchmark and a fine-grained training framework to evaluate writing reward models. |
| Outcome: | The proposed model improves on various writing benchmarks and exhibits strong generalization. |
Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following (2026.acl-long)
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| Challenge: | Existing reinforcement learning approaches suffer from dependency on external supervision and sparse reward signals from multi-constraint tasks. |
| Approach: | They propose a self-supervised reinforcement learning framework that eliminates dependency on external supervision by deriving reward signals directly from instructions and generating pseudo-labels for reward model training. |
| Outcome: | The proposed framework achieves strong improvements across 3 in-domain and 5 out-of-domain datasets while maintaining computational efficiency. |
Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following (2025.findings-acl)
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| Challenge: | Existing large language models struggle to follow multi-constraint instructions in real-world applications. |
| Approach: | They propose to quantify the difficulty distribution of constraints by a novel Difficulty Distribution Index (CDDI) they find that LLMs are more performant when presented with constraints in a “hard-to-easy” order. |
| Outcome: | The proposed model is more performant when presented with constraints in a “hard-to-easy” order, compared with existing models with different architectures and sizes of parameters. |
Beyond Correctness: Confidence-Aware Reward Modeling for Enhancing Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have shifted the post-training paradigm from instruction tuning and human preference alignment to reinforcement learning (RL) based on rule-based evaluations of answer correctness, these models often receive rewards for speculative answers without generating coherent reasoning chains. |
| Approach: | They propose a confidence-based reward model tailored for enhancing STEM reasoning capabilities. |
| Outcome: | The proposed model outperforms state-of-the-art open-source reward models across diverse STEM benchmarks. |