ScopeIF: Improving Scope-Aware Precise Instruction-Following in Large Language Models via Graded Reward Modeling

arXiv:2609.32189 · cs.CL · Submitted 2026-09-26 · Read on arXiv

cs.CL

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/thu-coai/ScopeIF

Terminology

• Verification — 55× in this paper · explained in 2 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• GRPO — 6× in this paper · explained in 6 episodes
• LLM judge — 6× in this paper · explained in 2 episodes
• Verifier — 6× in this paper · explained in 2 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Tool-Grounded Verification — 5× in this paper
• Benchmarking — 3× in this paper · explained in 4 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• Instruction-Level Accuracy — 3× in this paper
• Sparsity — 3× in this paper · explained in 4 episodes
• Constraint Success Rate — 2× in this paper
• Constraint-Level Accuracy — 2× in this paper
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Instruction Success Rate — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes

Sources

• ChatGLM-RLHF: Practices of Aligning Large Language Models with Human Feedback
• Adam: A Method for Stochastic Optimization
• Tulu 3: Pushing Frontiers in Open Language Model Post-Training
• RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data
• Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
• Decoupled Weight Decay Regularization
• IFHierBench: Hierarchical Instruction Following for Large Language Models
• GPQA: A Graduate-Level Google-Proof Q&A Benchmark
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models
• Qwen3 Technical Report
• Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following
• LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models
• A Survey of Large Language Models
• Instruction-Following Evaluation for Large Language Models

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