Reasoning LLMs-as-Judges, which benefit from inference-time scaling, offer a promising path for extending the success of reasoning models to non-verifiable domains, where output quality cannot be directly checked. While reasoning judges have shown gains on static evaluation benchmarks, their impact on actual LLM post-training remains underexplored. Therefore, we conduct a controlled study of non-reasoning and reasoning LLM-judges in reinforcement-learning-based LLM alignment. In our synthetic setting, a "gold" judge provides preference annotations for training smaller judges and evaluates the resulting policies. We find that non-reasoning judges lead to the standard reward hacking pattern: policies receive higher rewards from the training judge but lower rewards from the gold judge. Reasoning judges, in contrast, produce policies with high gold-judge rewards. However, a qualitative analysis shows that these high rewards arise from adversarial outputs that exploit the gold judge’s preferences and transfer to other LLM-judges on benchmarks like Arena-Hard. Together with our ablations on judge training design choices, these findings reveal both the promise and risks of using reasoning LLM-judges for non-verifiable LLM post-training.

Autoregressive (AR) encoder-decoder neural networks have proved successful in many NLP problems, including Semantic Parsing – a task that translates natural language to machine-readable parse trees. However, the sequential prediction process of AR models can be slow. To accelerate AR for semantic parsing, we introduce a new technique called TreePiece that tokenizes a parse tree into subtrees and generates one subtree per decoding step. On TOPv2 benchmark, TreePiece shows 4.6 times faster decoding speed than standard AR, and comparable speed but significantly higher accuracy compared to Non-Autoregressive (NAR).