Ekaterina Fadeeva

Test-time search for multi-step LLM reasoning has long traded between two imperfect signals: post-hoc voting keeps diversity but cannot steer generation toward correct paths, while beam-style verifier guidance intervenes early but over-concentrates and amplifies verifier errors. VGSC multiplies step-level correctness with a future-outcome prediction into an acceptance weight, applies bounded rejection-resampling to candidate steps during decoding, and keeps multiple trajectories for final self-consistency voting. Across five reasoning benchmarks with the Qwen2.5 Math 1.5B Instruct generator and Qwen2.5 Math PRM 7B verifier, VGSC reaches 63.1% average accuracy at verifier-trust exponent 10, above the strongest non-VGSC baseline BS+SC-Future at 60.7%.

Test-time search for multi-step LLM reasoning has long traded between two imperfect signals: post-hoc voting keeps diversity but cannot steer generation toward correct paths, while beam-style verifier guidance intervenes early but over-concentrates and amplifies verifier errors. VGSC multiplies step-level correctness with a future-outcome prediction into an acceptance weight, applies bounded rejection-resampling to candidate steps during decoding, and keeps multiple trajectories for final self-consistency voting. Across five reasoning benchmarks with the Qwen2.5 Math 1.5B Instruct generator and Qwen2.5 Math PRM 7B verifier, VGSC reaches 63.1% average accuracy at verifier-trust exponent 10, above the strongest non-VGSC baseline BS+SC-Future at 60.7%.