ReProbe: Efficient Test-Time Reasoning Expansion via Probing Internal States of Large Language Models

ReProbe is the official implementation of a paper accepted by ACL 2026, proposing a new method to efficiently expand test-time reasoning by probing the internal states of Large Language Models (LLMs). Its core idea is to use the internal hidden states of LLMs to guide the reasoning process, intelligently allocate computing resources, and reduce computational overhead while maintaining reasoning quality. Compared to traditional test-time expansion methods, ReProbe has significant advantages in computational efficiency, expansion accuracy, and generality, and performs excellently on multiple benchmark tasks such as mathematical reasoning, logical reasoning, and code generation.

The performance of large language models on complex reasoning tasks (such as mathematical problem solving and logical reasoning) is a core challenge in AI research, requiring multi-step thinking to arrive at correct answers. Test-time computational expansion is an important direction to improve reasoning ability, but traditional methods (such as sampling voting and tree search) have efficiency bottlenecks—they need to generate a large number of intermediate steps, leading to high computational costs. How to reduce overhead without sacrificing quality has become an urgent problem to solve.

The core idea of ReProbe is to delve into the model's interior and use hidden states to guide reasoning, inspired by cognitive science (human internal representations are richer than external expressions). Key mechanisms include:

1. Internal State Probing: Capture hidden representations of specific layers when generating each reasoning step, estimate confidence, and achieve early termination;
2. Selective Computational Expansion: Increase sampling/search depth for difficult problems, reduce computation for simple problems, and dynamically prune low-quality paths.

State Probe Design

Train lightweight probe networks to interpret the internal states of LLMs, with features: layer selectivity (focusing on information-rich middle layers), task adaptability (training specialized probes for different reasoning tasks), and lightweight efficiency (small number of parameters, negligible overhead).

Reasoning Process Monitoring

1. Step-level evaluation: Evaluate quality immediately after generating each step;
2. Trajectory-level prediction: Predict the success probability of a path by integrating multi-step states;
3. Decision point identification: Perform computational expansion at key nodes.

Comparison with Existing Methods

Method Type	Computational Efficiency	Expansion Accuracy	Generality
Naive Sampling	Low	Medium	High
Tree Search	Very Low	High	Medium
ReProbe	High	High	High

Benchmark Performance

On benchmarks such as mathematical reasoning (GSM8K, MATH), logical reasoning (LSAT, logic puzzles), and code generation (HumanEval, MBPP), it significantly outperforms baselines under the same computational budget.

Computational Efficiency Improvement

• For some tasks, only 30-50% of the computational volume of traditional methods is needed to achieve the same effect;
• Speedup of 5-10 times for simple problems;
• Significant reduction in overall reasoning latency.

Ablation Experiment Insights

• Internal state signals reflect reasoning quality more accurately than output confidence;
• Layer selection strategy has a significant impact on performance;
• Lightweight probe design is the key to efficiency advantages.

Practical Application Value

• API Cost Optimization: Reduce reasoning calls and lower the operational cost of commercial LLM APIs;
• Real-time Interaction Scenarios: Improve response speed in scenarios such as dialogue systems and online tutoring;
• Edge Deployment: Intelligently allocate computing to enable edge devices to perform complex reasoning.

Current Limitations

• Probe training requires additional data and computation;
• Optimized for specific model architectures, requiring adjustments when migrating to new models;
• The mechanism linking internal states and reasoning quality is not fully clear.

Future Research Directions

• Develop universal probing mechanisms across tasks and models;
• Implement online learning for probes to continuously improve from actual reasoning;
• Extend internal state probing to multi-modal tasks such as visual reasoning.

Summary

ReProbe is an important advancement in the field of test-time computational expansion. By mining the internal states of LLMs, it achieves more intelligent and efficient reasoning. It not only provides a practical technical solution but also offers a new perspective for understanding the reasoning mechanisms of LLMs. As the application of large models becomes widespread, such efficiency optimization technologies will become increasingly important.