Incentivizing Reasoning for Advanced Instruction-Following of Large
  Language Models
Authors: name: Yulei Qin, name: Gang Li, name: Zongyi Li, name: Zihan Xu, name: Yuchen Shi, name: Zhekai Lin, name: Xiao Cui, name: Ke Li, name: Xing Sun
Organization: Tencent

Existing large language models (LLMs) face challenges of following complex
instructions, especially when multiple constraints are present and organized in
paralleling, chaining, and branching structures. One intuitive solution, namely
chain-of-thought (CoT), is expected to universally improve capabilities of
LLMs. However, we find that the vanilla CoT exerts a negative impact on
performance due to its superficial reasoning pattern of simply paraphrasing the
instructions. It fails to peel back the compositions of constraints for
identifying their relationship across hierarchies of types and dimensions. To
this end, we propose a systematic method to boost LLMs in dealing with complex
instructions via incentivizing reasoning for test-time compute scaling. First,
we stem from the decomposition of complex instructions under existing
taxonomies and propose a reproducible data acquisition method. Second, we
exploit reinforcement learning (RL) with verifiable rule-centric reward signals
to cultivate reasoning specifically for instruction following. We address the
shallow, non-essential nature of reasoning under complex instructions via
sample-wise contrast for superior CoT enforcement. We also exploit behavior
cloning of experts to facilitate steady distribution shift from fast-thinking
LLMs to skillful reasoners. Extensive evaluations on seven comprehensive
benchmarks confirm the validity of the proposed method, where a 1.5B LLM
achieves 11.74% gains with performance comparable to a 8B LLM. Codes and data
are available at https://github.com/yuleiqin/RAIF.