Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies

Authors

Yuqiao Tan; Minzheng Wang; Shizhu He; Huanxuan Liao; Chengfeng Zhao; Qiunan Lu; Tian Liang; Jun Zhao; Kang Liu

Scores

Rationale

The paper introduces a novel approach to reinforcement learning by decomposing language model policies into internal layer and modular policies, which is a fresh perspective on optimizing LLMs. This method addresses the technical bottleneck of improving reasoning in LLMs by aligning training objectives at lower layers, potentially enhancing model performance in complex tasks. While the method is primarily focused on LLMs, the concept of internal policy decomposition could be applicable to other architectures, providing moderate transferability. The approach aligns well with ongoing research trends in understanding and optimizing LLMs. The evidence presented is solid, with extensive experiments on reasoning benchmarks, but further validation in diverse domains would strengthen its robustness. The idea holds promise for long-term impact by improving foundational reasoning capabilities in AI systems.