Papers by Zeju Li
Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier (2026.acl-long)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have enhanced capabilities in complex reasoning through step-by-step trace generation. |
| Approach: | They propose a generative verifier that dynamically allocates compute between rapid fast thinking and deliberative slow thinking. |
| Outcome: | The proposed solution outperforms GenPRM-32B on ProcessBench while requiring 2.3x fewer TFLOPS and 15x less training data. |
ThinkAnswer Loss: Balancing Semantic Similarity and Exact Matching for LLM Reasoning Enhancement (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for knowledge distillation use Chain-of-Thought (CoT) and answer pairs, but they lack appropriate supervision signals. |
| Approach: | They propose a framework that decouples CoT and answer supervision . the framework applies semantic similarity constraints while maintaining strict literal matching for the answer . |
| Outcome: | The proposed framework decouples CoT and answer supervision while maintaining strict literal matching for the answer. |
Dyve: Thinking Fast and Slow for Dynamic Process Verification (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing process verification methods struggle with reliably assessing incomplete reasoning traces and are limited by the cost of high-quality human annotations or the inherent noise in automatically generated labels. |
| Approach: | They propose a dynamic process verifier that integrates fast and slow thinking to enhance reasoning error detection in large language models. |
| Outcome: | The proposed system outperforms existing process-based verifiers and maintains computational efficiency while maintaining high performance. |