@inproceedings{tan-etal-2026-bottom,
title = "Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies",
author = "Tan, Yuqiao and
Wang, Minzheng and
He, Shizhu and
Liao, Huanxuan and
Zhao, Chengfeng and
Liang, Tian and
Zhao, Jun and
Liu, Kang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1173/",
pages = "26891--26910",
ISBN = "979-8-89176-459-0",
abstract = "Existing reinforcement learning (RL) approaches treat large language models (LLMs) as a unified policy, overlooking their internal mechanisms. In this paper, we decompose the LLM-based policy into Internal Layer Policies and Internal Modular Policies via the Transformer{'}s residual stream. Our entropy analysis of internal policy reveals distinct patterns: (1) universally, internal policies evolve from high-entropy exploration in early layers to deterministic refinement in the top layers; and (2) Qwen exhibits an explicit progressive reasoning structure, contrasting with the abrupt convergence in Llama. Furthermore, we discover that optimizing internal layers induces feature refinement, forcing lower layers to capture high-level reasoning representations early. Motivated by these findings, we propose Bottom-up Policy Optimization (BuPO), a novel RL paradigm that reconstructs the LLM{'}s reasoning foundation from the bottom up by optimizing internal layers in early stages. Extensive experiments on complex reasoning benchmarks demonstrate the effectiveness of BuPO."
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%0 Conference Proceedings
%T Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies
%A Tan, Yuqiao
%A Wang, Minzheng
%A He, Shizhu
%A Liao, Huanxuan
%A Zhao, Chengfeng
%A Liang, Tian
%A Zhao, Jun
%A Liu, Kang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F tan-etal-2026-bottom
%X Existing reinforcement learning (RL) approaches treat large language models (LLMs) as a unified policy, overlooking their internal mechanisms. In this paper, we decompose the LLM-based policy into Internal Layer Policies and Internal Modular Policies via the Transformer’s residual stream. Our entropy analysis of internal policy reveals distinct patterns: (1) universally, internal policies evolve from high-entropy exploration in early layers to deterministic refinement in the top layers; and (2) Qwen exhibits an explicit progressive reasoning structure, contrasting with the abrupt convergence in Llama. Furthermore, we discover that optimizing internal layers induces feature refinement, forcing lower layers to capture high-level reasoning representations early. Motivated by these findings, we propose Bottom-up Policy Optimization (BuPO), a novel RL paradigm that reconstructs the LLM’s reasoning foundation from the bottom up by optimizing internal layers in early stages. Extensive experiments on complex reasoning benchmarks demonstrate the effectiveness of BuPO.
%U https://aclanthology.org/2026.emnlp-main.1173/
%P 26891-26910
Markdown (Informal)
[Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies](https://aclanthology.org/2026.emnlp-main.1173/) (Tan et al., EMNLP 2026)
ACL
- Yuqiao Tan, Minzheng Wang, Shizhu He, Huanxuan Liao, Chengfeng Zhao, Tian Liang, Jun Zhao, and Kang Liu. 2026. Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 26891–26910, Budapest, Hungary. Association for Computational Linguistics.