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This paper finds that prior audit and repair episodes in context reduce false alarms in LLM verifiers by shifting decision thresholds, with repair content and audit verdict complementarily affecting different model families.
The paper introduces verifier-induced support reshaping, showing that on-policy RL with verifiable rewards can improve the current objective while making successful behaviors for later objectives too rare to sample. Experiments across math reasoning and instruction following demonstrate that endpoint improvements do not guarantee future trainability.
The author discusses the lack of intermediate representation (IR) and verifier in the deployment of AI in serious industries, using text-to-CAD as an example to illustrate the key role of unified IR and verification in the feasibility of AI solutions.
The paper introduces VGB, a process-guided sampling algorithm with probabilistic backtracking, which significantly improves coding performance on tiny 0.5B models by being robust to verifier errors.
Sign-Gated On-Policy Distillation (SG-OPD) enhances standard on-policy distillation by using a binary verifier as a trust signal for teacher supervision, improving performance on competition-level math reasoning benchmarks.
This paper studies verifier-backed committee search as inference-time boosting for reasoning language models, showing that a committee of weak reasoning models can match the performance of much stronger models on code repair tasks like SWE-bench Verified.
Introduces LoVer, an unsupervised verifier that uses logical rules (negation consistency, intra-group and inter-group consistency) to improve LLM reasoning without labeled data, achieving performance close to supervised verifiers on reasoning benchmarks.