Progressive Multimodal Reasoning via Active Retrieval
Multi-step multimodal reasoning tasks pose significant challenges for
multimodal large language models (MLLMs), and finding effective ways to enhance
their performance in such scenarios remains an unresolved issue. In this paper,
we propose AR-MCTS, a universal framework designed to progressively improve the
reasoning capabilities of MLLMs through Active Retrieval (AR) and Monte Carlo
Tree Search (MCTS). Our approach begins with the development of a unified
retrieval module that retrieves key supporting insights for solving complex
reasoning problems from a hybrid-modal retrieval corpus. To bridge the gap in
automated multimodal reasoning verification, we employ the MCTS algorithm
combined with an active retrieval mechanism, which enables the automatic
generation of step-wise annotations. This strategy dynamically retrieves key
insights for each reasoning step, moving beyond traditional beam search
sampling to improve the diversity and reliability of the reasoning space.
Additionally, we introduce a process reward model that aligns progressively to
support the automatic verification of multimodal reasoning tasks. Experimental
results across three complex multimodal reasoning benchmarks confirm the
effectiveness of the AR-MCTS framework in enhancing the performance of various
multimodal models. Further analysis demonstrates that AR-MCTS can optimize
sampling diversity and accuracy, yielding reliable multimodal reasoning.
6764d821772d134796df3f97
Guanting Dong
admin_assigned
2024-12-20T09:16:05.803Z
61cd4b833dd34ba1985e0753
https://cdn-avatars.huggingface.co/v1/production/uploads/61cd4b833dd34ba1985e0753/BfHfrwotoMESpXZOHiIe4.png
KABI
user
dongguanting
6764d821772d134796df3f98
Chenghao Zhang
claimed_verified
2024-12-20T08:33:01.155Z
6710ac3fb4ee4920580a5f0e
https://cdn-avatars.huggingface.co/v1/production/uploads/6710ac3fb4ee4920580a5f0e/OhQQFlZmkmLQpMYqKCGP6.jpeg
Chenghao Zhang
user
SnowNation
6764d821772d134796df3f99
Mengjie Deng
admin_assigned
2024-12-20T09:16:18.270Z
65db23d1f386d08eb0d1cec5
/avatars/a0ad895fb34638563c36c93054c4dc5e.svg
Mengjie Deng
user
MengjieDeng
6764d821772d134796df3f9a
Yutao Zhu
admin_assigned
2024-12-20T09:16:23.877Z
625e62452a7279d3c77b5c38
https://cdn-avatars.huggingface.co/v1/production/uploads/625e62452a7279d3c77b5c38/zJINew6U4_Gup4WTobb-0.jpeg
Yutao Zhu
user
yutaozhu94
6764d821772d134796df3f9b
Zhicheng Dou
admin_assigned
2024-12-20T09:16:29.532Z
66f0bf59e9d50ec57febf751
/avatars/be97941e60064e5dd806c6fe9db3c537.svg
Zhicheng Dou
user
douzc
6764d821772d134796df3f9c
Ji-Rong Wen
admin_assigned
2024-12-20T09:16:35.274Z
64b8c89052b7353d8c6a1013
/avatars/cd59fffe81f6b07b4519540b8ff3d95f.svg
Ji-Rong Wen
user
jrwen
Progressive Multimodal Reasoning via Active Retrieval
Multi-step multimodal reasoning tasks pose significant challenges for
multimodal large language models (MLLMs), and finding effective ways to enhance
their performance in such scenarios remains an unresolved issue. In this paper,
we propose AR-MCTS, a universal framework designed to progressively improve the
reasoning capabilities of MLLMs through Active Retrieval (AR) and Monte Carlo
Tree Search (MCTS). Our approach begins with the development of a unified
retrieval module that retrieves key supporting insights for solving complex
reasoning problems from a hybrid-modal retrieval corpus. To bridge the gap in
automated multimodal reasoning verification, we employ the MCTS algorithm
combined with an active retrieval mechanism, which enables the automatic
generation of step-wise annotations. This strategy dynamically retrieves key
insights for each reasoning step, moving beyond traditional beam search
sampling to improve the diversity and reliability of the reasoning space.
Additionally, we introduce a process reward model that aligns progressively to
support the automatic verification of multimodal reasoning tasks. Experimental
results across three complex multimodal reasoning benchmarks confirm the
effectiveness of the AR-MCTS framework in enhancing the performance of various
multimodal models. Further analysis demonstrates that AR-MCTS can optimize
sampling diversity and accuracy, yielding reliable multimodal reasoning.
AR-MCTS enhances multimodal large language models' reasoning capabilities through active retrieval, Monte Carlo Tree Search, and a process reward model, improving performance across multimodal reasoning tasks.
Active Retrieval Monte Carlo Tree Search unified retrieval module automated multimodal reasoning verification beam search sampling process reward model multimodal reasoning benchmarks