AgentTTS: Large Language Model Agent for Test-time Compute-optimal
  Scaling Strategy in Complex Tasks
Test-time scaling (TTS) enhances the performance of large language models
(LLMs) by allocating additional compute resources during inference. However,
existing research primarily investigates TTS in single-stage tasks; while many
real-world problems are multi-stage complex tasks, composed of a sequence of
heterogeneous subtasks with each subtask requires LLM of specific capability.
Therefore, we study a novel problem: the test-time compute-optimal scaling in
multi-stage complex tasks, aiming to select suitable models and allocate
budgets per subtask to maximize overall performance. TTS in multi-stage tasks
introduces two fundamental challenges: (i) The combinatorial search space of
model and budget allocations, combined with the high cost of inference, makes
brute-force search impractical. (ii) The optimal model and budget allocations
across subtasks are interdependent, increasing the complexity of the
compute-optimal search. To address this gap, we conduct extensive pilot
experiments on four tasks across six datasets, deriving three empirical
insights characterizing the behavior of LLMs in multi-stage complex tasks.
Informed by these insights, we propose AgentTTS, an LLM-agent-based framework
that autonomously searches for compute-optimal allocations through iterative
feedback-driven interactions with the execution environment. Experimental
results demonstrate that AgentTTS significantly outperforms traditional and
other LLM-based baselines in search efficiency, and shows improved robustness
to varying training set sizes and enhanced interpretability.
68919443f01a094725f83574
Fali Wang
68919443f01a094725f83575
Hui Liu
68919443f01a094725f83576
Zhenwei Dai
68919443f01a094725f83577
Jingying Zeng
68919443f01a094725f83578
Zhiwei Zhang
68919443f01a094725f83579
Zongyu Wu
68919443f01a094725f8357a
Chen Luo
68919443f01a094725f8357b
Zhen Li
68919443f01a094725f8357c
Xianfeng Tang
68919443f01a094725f8357d
Qi He
68919443f01a094725f8357e
Suhang Wang
AgentTTS: Large Language Model Agent for Test-time Compute-optimal
  Scaling Strategy in Complex Tasks
Test-time scaling (TTS) enhances the performance of large language models
(LLMs) by allocating additional compute resources during inference. However,
existing research primarily investigates TTS in single-stage tasks; while many
real-world problems are multi-stage complex tasks, composed of a sequence of
heterogeneous subtasks with each subtask requires LLM of specific capability.
Therefore, we study a novel problem: the test-time compute-optimal scaling in
multi-stage complex tasks, aiming to select suitable models and allocate
budgets per subtask to maximize overall performance. TTS in multi-stage tasks
introduces two fundamental challenges: (i) The combinatorial search space of
model and budget allocations, combined with the high cost of inference, makes
brute-force search impractical. (ii) The optimal model and budget allocations
across subtasks are interdependent, increasing the complexity of the
compute-optimal search. To address this gap, we conduct extensive pilot
experiments on four tasks across six datasets, deriving three empirical
insights characterizing the behavior of LLMs in multi-stage complex tasks.
Informed by these insights, we propose AgentTTS, an LLM-agent-based framework
that autonomously searches for compute-optimal allocations through iterative
feedback-driven interactions with the execution environment. Experimental
results demonstrate that AgentTTS significantly outperforms traditional and
other LLM-based baselines in search efficiency, and shows improved robustness
to varying training set sizes and enhanced interpretability.
AgentTTS, an LLM-agent-based framework, optimizes compute allocation for multi-stage complex tasks, improving performance and robustness compared to traditional methods.
test-time scaling large language models multi-stage complex tasks combinatorial search space inference cost compute-optimal scaling AgentTTS iterative feedback-driven interactions