R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth
  and Depth?
Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1,
DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought
(CoT). However, existing benchmarks mainly focus on immediate, single-horizon
tasks, failing to adequately evaluate models' ability to understand and respond
to complex, long-horizon scenarios. To address this incomplete evaluation of
Large Reasoning Models (LRMs), we propose R-HORIZON, a method designed to
stimulate long-horizon reasoning behaviors in LRMs through query composition.
Based on R-HORIZON, we construct a long-horizon reasoning benchmark, comprising
complex multi-step reasoning tasks with interdependent problems that span long
reasoning horizons. Through comprehensive evaluation of LRMs using the
R-HORIZON benchmark, we find that even the most advanced LRMs suffer
significant performance degradation. Our analysis reveals that LRMs exhibit
limited effective reasoning length and struggle to allocate thinking budget
across multiple problems appropriately. Recognizing these limitations, we use
R-HORIZON to construct long-horizon reasoning data for reinforcement learning
with verified rewards (RLVR). Compared to training with single-horizon data,
RLVR with R-HORIZON not only substantially improves performance on the
multi-horizon reasoning tasks, but also promotes accuracy on standard reasoning
tasks, with an increase of 7.5 on AIME2024. These results position R-HORIZON as
a scalable, controllable, and low-cost paradigm for enhancing and evaluating
the long-horizon reasoning capabilities of LRMs.
68e9eab7012880a10864b725
Yi Lu
claimed_verified
2025-10-11T13:26:23.631Z
61fde97843eb0913fa2df67b
/avatars/6b739fa8ab23ba69accb5614d96b243b.svg
Luyi
user
lulululuyi
68e9eab7012880a10864b726
Jianing Wang
68e9eab7012880a10864b727
Linsen Guo
claimed_verified
2025-10-15T15:25:32.307Z
651581635b041d575d08e549
/avatars/45a4d6f8257d1808c7452eefb6b20720.svg
linsen GUO
user
sooonas
68e9eab7012880a10864b728
Wei He
claimed_verified
2025-10-15T07:08:07.179Z
66ecee857264238429a1211f
https://cdn-avatars.huggingface.co/v1/production/uploads/66ecee857264238429a1211f/TbuM7ToLBrSxDF8mOccpK.jpeg
Wei He
user
hewei2001
68e9eab7012880a10864b729
Hongyin Tang
68e9eab7012880a10864b72a
Tao Gui
68e9eab7012880a10864b72b
Xuanjing Huang
68e9eab7012880a10864b72c
Xuezhi Cao
68e9eab7012880a10864b72d
Wei Wang
68e9eab7012880a10864b72e
Xunliang Cai
R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth
  and Depth?
Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1,
DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought
(CoT). However, existing benchmarks mainly focus on immediate, single-horizon
tasks, failing to adequately evaluate models' ability to understand and respond
to complex, long-horizon scenarios. To address this incomplete evaluation of
Large Reasoning Models (LRMs), we propose R-HORIZON, a method designed to
stimulate long-horizon reasoning behaviors in LRMs through query composition.
Based on R-HORIZON, we construct a long-horizon reasoning benchmark, comprising
complex multi-step reasoning tasks with interdependent problems that span long
reasoning horizons. Through comprehensive evaluation of LRMs using the
R-HORIZON benchmark, we find that even the most advanced LRMs suffer
significant performance degradation. Our analysis reveals that LRMs exhibit
limited effective reasoning length and struggle to allocate thinking budget
across multiple problems appropriately. Recognizing these limitations, we use
R-HORIZON to construct long-horizon reasoning data for reinforcement learning
with verified rewards (RLVR). Compared to training with single-horizon data,
RLVR with R-HORIZON not only substantially improves performance on the
multi-horizon reasoning tasks, but also promotes accuracy on standard reasoning
tasks, with an increase of 7.5 on AIME2024. These results position R-HORIZON as
a scalable, controllable, and low-cost paradigm for enhancing and evaluating
the long-horizon reasoning capabilities of LRMs.
R-HORIZON, a method using query composition, improves long-horizon reasoning in Large Reasoning Models through a benchmark of complex multi-step tasks, enhancing performance and accuracy.
Chain-of-Thought CoT Large Reasoning Models LRMs long-horizon reasoning query composition reinforcement learning with verified rewards RLVR AIME2024