原文依据： 「We find that most models strongly overfit on a small set of verifiable constraints from the benchmarks that test these abilities, a skill called precise instruction following, and are not able to generalize well to unseen output constraints.」（出自：论文，第1页）
原文依据： 「We introduce a new benchmark, IFBENCH, to evaluate precise instruction following generalization on 58 new, diverse, and challenging verifiable out-of-domain constraints.」（出自：论文，第1页）
知识点： 论文提出并验证了一种名为IF-RLVR的训练方法，它利用“带可验证奖励的强化学习”（Reinforcement Learning with Verifiable Rewards）来显著提升模型的精确指令遵循能力。 题目： 论文中提出的用以提升模型精确指令遵循能力的核心训练方法是什么？ 选项：
A. 直接偏好优化 (DPO)✅
B. 有监督微调 (SFT)✅
C. 带可验证奖励的强化学习 (RLVR)✅
D. 上下文学习 (In-context Learning)✅
正确答案： C
原文依据： 「Specifically, we carefully design constraint verification modules and show that reinforcement learning with verifiable rewards (RLVR) significantly improves instruction following.」（出自：论文，第1页）
解析： 论文重点介绍并证明了RLVR（Reinforcement Learning with Verifiable Rewards）方法在提升精确指令遵循（IF）能力方面的有效性。该方法通过为满足约束的输出提供奖励信号来训练模型。虽然论文也对比了DPO，但RLVR是其推荐和研究的核心方法。
C. 模型在IFEval上得分很高（如超过80%），但在IFBENCH上得分很低（如低于50%），显示出明显的性能差异和泛化问题。✅
D. 两个基准测试的难度相当，模型的得分波动主要是由模型大小决定的。✅
正确答案： C
原文依据： 「…leading models such as GPT-4.1 or Claude 3.7 Sonnet score below 50%. … This shows that most state-of-the-art models overfit on IFEval and are not able to generalize well…」（出自：论文，第1页及第3页图1）
原文依据： 「…it can sometimes result in models that over-prioritize the constraint over the full instruction. An example of such an output is given in Figure 8. This could also be called over-optimization.」（出自：论文，第10页）
原文依据： 「We propose adding a general reward model (RM) signal to the verifiable reward. The intuition is that while the verifiable reward checks for the adherence to the output constraint, the general reward model provides signal for whether the response answers the prompt.」（出自：论文，第10页）
原文依据： 「…despite training on the same prompts and starting from the same model, GRPO training with IF verifiable rewards consistently outperforms the model trained with DPO on IFEval and IFBENCH.」（出自：论文，第8页）
原文依据： 「Following (verifiable) output constraints can stand in conflict with following the main task mentioned in the instruction and a model has to trade-off between completing the task while also adhering to the constraint.」（出自：论文，第9页）
原文依据： 「We exclusively focus on verifiable constraints, which is limiting, as many constraints used by users in the wild are constraints that do not have an easily verifiable ground truth. This also means our constraints might sometimes seem unnatural or contrived.」（出自：论文，第11页）
解析： 在第7节“Conclusion and Limitations”中，作者明确指出，他们的工作“专门关注可验证的约束，这是有局限性的”，因为现实世界中的用户约束往往难以用程序自动验证，有时可能显得“不自然或做作”。
原文依据： 「29 new training constraints and verification functions, IFTRAIN, to enable simple data creation that improves instruction following performance.」（出自：论文，第2页）
原文依据： 「Interestingly, though, training on a wider variable range, performs comparably and often even better than training on the same range. This suggests that training on a diverse set of constraint variables improves generalization…」（出自：论文，第6页）
原文依据： 「In Table 6, we find that IF-RLVR training a base model leads to nearly the same IFEval performance as when using an instruct policy.」（出自：论文，第8页）
原文依据： 「We also see that targeted RLVR training for IF slightly harms other downstream evaluations, such as AlpacaEval 2, while staying comparable on others, such as GSM8K, MMLU and BBH.」（出自：论文，第7页）
原文依据： 「The task of precise instruction following (IF) evaluates a language model’s ability to perform a task t, such as summarization or creative writing, while adhering to one or more output constraints c, which can be automatically verified.」（出自：论文，第2页）
原文依据： 「The prompts for verifiable IF training are created by combining an instruction from a public SFT dataset with a constraint from either the IFEval taxonomy … or our new unseen training constraint taxonomy…」（出自：论文，第4页）
原文依据： 「We designed the new training constraints so that they would cover IF skills models are currently lacking in, such as copying from the input, counting, and formatting.」（出自：论文，第7页）
原文依据： 「Reinforcement Learning with Verifiable Rewards (RLVR) … as each constraint can be verified with a function. We use GRPO … where each output is scored according to wether or not the constraint has been correctly fulfilled.」（出自：论文，第4页）
学习目标
通过精心设计的选择题和原文对照，帮助学习者掌握关于论文《Generalizing Verifiable Instruction Following》的核心知识点，理解大型语言模型在精确指令遵循方面的挑战、评测方法及提升策略。
使用说明
请仔细阅读每个问题，选择你认为正确的答案，然后对照参考答案和解析。通过原文依据，加深对关键概念的理解。
题目与解析