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Pull request overview
This PR forward-resyncs the Chinese translation of lectures/jv.md to match the upstream source lecture, replacing the older NumPy/Numba-based implementation with the current JAX-based approach and updating surrounding lecture structure to align with the source.
Changes:
- Resynced the entire
jv.mdlecture content (title, section structure, added GPU admonition include). - Replaced the previous
JVWorker/Numba/nested-loop solution code with a JAXNamedTuple+vmap+lax.while_loopimplementation. - Added translation sync state metadata in
.translate/state/jv.md.yml.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 4 comments.
| File | Description |
|---|---|
| lectures/jv.md | Whole-file resync to upstream content; switches model solution to JAX and adds GPU-related include/note. |
| .translate/state/jv.md.yml | Records the source commit SHA and resync metadata for translation tooling. |
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| """ | ||
| 创建一个在职搜索模型的实例。 | ||
| """ | ||
| f_rvs = jr.beta(jr.key(seed), a, b, (mc_size,)) |
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| ```{code-block} python3 | ||
| jv = JVWorker(grid_size=25, mc_size=50) | ||
| ```{code-block} ipython3 |
| """ | ||
| 对plot_grid中的每个x，根据上面给出的x_{t+1}运动规律，抽取下一期资本的实现值。 | ||
| 返回一个形状为(len(plot_grid), mc_size)的数组。 | ||
| """ |
| return jnp.where(b, jnp.maximum(gxϕ, u), gxϕ) | ||
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| x_next = simulate_next(jr.key(1234), plot_grid) |
✅ Translation Quality ReviewVerdict: PASS | Model: claude-sonnet-5 | Date: 2026-07-21 📝 Translation Quality
Summary: This is a high-quality, comprehensive resync translation that accurately conveys the technical content of the on-the-job search model, including JAX implementation details, dynamic programming concepts, and economic intuition. Minor issues include an English/Chinese mix in one index directive and a small deviation in phrasing for the back-of-envelope section intro, plus a minor formatting inconsistency in plot titles where math mode was added unnecessarily. Overall terminology, fluency, and formatting are excellent and consistent with the established glossary. Mathematical notation and LaTeX equations are preserved accurately throughout all changed sections Code blocks and comments are consistently and accurately translated, maintaining code functionality context (e.g., variable descriptions in NamedTuple) Technical dynamic programming and JAX-specific terminology (贝尔曼算子, 贝尔曼方程, 向量化, 蒙特卡洛) is applied consistently per glossary standards Complex explanatory passages (e.g., vmap triple-loop explanation, Bellman equation derivation) are translated fluently while preserving technical precision Suggestions:
🔍 Diff Quality
Summary: The target document was correctly resynced to match the current English source, with the JAX-based rewrite fully translated and properly positioned, and translation metadata accurately reflects the updated headings. This review was generated automatically by action-translation review mode. |
Forward Resync: jv.md
Source: QuantEcon/lecture-python.myst — lectures/jv.md
Source commit:
2f1eeadThis PR resyncs the translation to match the current source document.
Reason: The target uses an entirely different, older implementation (NumPy/Numba with a JVWorker class, nested loops, operator_factory) instead of the source's JAX-based implementation (NamedTuple, vmap, lax.while_loop). This is a substantive divergence in code content, not just style. Also missing the
{include} _admonition/gpu.mdblock and the note about JAX/GPU scaling, and the title says 'VI' instead of 'VII'. No major additions beyond i18n font setup, so CONTENT_CHANGES is the correct verdict.Changes
Whole-file resync applied. The entire document was resynced in a single pass.
Created by action-translation forward resync