Machine translation systems still make systematic errors on idioms and other expressions that cannot be translated literally. Model editing offers a possible way to fix such recurring errors without retraining the whole model, but it has mostly been studied for monolingual factual updates rather than translation. We introduce MEMT-bench, the first benchmark for evaluating model editing in machine translation across multiple languages, models, and editing settings. The benchmark is built from idiomatic translation errors and tests whether an edit can correct a targeted mistranslation while preserving unrelated translation behavior. Experiments with causal tracing and representative editing methods reveal both the promise and difficulty of translation editing: edits can repair targeted errors, but maintaining cross-lingual locality and robustness under repeated edits remains challenging. Code and data are publicly available at [GitHub](https://github.com/dannalily/MEMT-Model-Editing-for-Targeted-Translation-Error-Repair/) and [Hugging Face](https://huggingface.co/datasets/Dannalily/MEMT-bench), respectively.

Large Language Models (LLMs) often suffer from mode collapse, repeatedly generating the same few completions even when many valid answers exist, limiting their diversity across a wide range of tasks. We introduce Group-Aware Policy Optimization (GAPO), a simple extension of the recent and popular Group Relative Policy Optimization (GRPO) that computes rewards over the group as a whole. GAPO enables learning from the group-level properties such as diversity and coverage.We demonstrate GAPO using a frequency-aware reward function that encourages uniform sampling over valid LLM completions, and show that GAPO-trained models produce valid and more diverse model responses.Beyond this setup, GAPO generalizes to open-ended prompts and improves response diversity without compromising accuracy on standard LLM benchmarks (GSM8K, MATH, HumanEval, MMLU-Pro). Our code will be made publicly available.