Polish SkillGym dataset card

README.md

@@ -20,29 +20,40 @@ tags:
 <div align="center">
 
 <picture>
-  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/ECNU-ICALK/SkillGym/main/assets/brand-dark.png">
-  <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/ECNU-ICALK/SkillGym/main/assets/brand-light.png">
-  <img src="https://raw.githubusercontent.com/ECNU-ICALK/SkillGym/main/assets/brand-light.png" alt="SkillGym" width="520">
+  <source media="(prefers-color-scheme: dark)"
+          srcset="https://raw.githubusercontent.com/ECNU-ICALK/SkillGym/main/assets/brand-dark.png">
+  <source media="(prefers-color-scheme: light)"
+          srcset="https://raw.githubusercontent.com/ECNU-ICALK/SkillGym/main/assets/brand-light.png">
+  <img
+    src="https://raw.githubusercontent.com/ECNU-ICALK/SkillGym/main/assets/brand-light.png"
+    alt="SkillGym"
+    width="360">
 </picture>
 
-### SkillGym Dataset
+<h2>SkillGym Dataset</h2>
 
-**Human-written skills, executable environments, and verified long-horizon agent trajectories**
+<p>
+  <strong>Human-written skills, executable environments, and verified long-horizon agent trajectories</strong>
+</p>
 
-[GitHub / Code](https://github.com/ECNU-ICALK/SkillGym) ·
-[SkillGym-Agent](https://huggingface.co/ecnu-icalk/SkillGym-Agent) ·
-[Paper](https://github.com/ECNU-ICALK/SkillGym/blob/main/assets/Internalizing_Large_Scale_Human_Written_Skills_into_LLMs_for_Real_World_Problem_Solving.pdf)
+<p>
+  <strong>English</strong> ·
+  <a href="README_zh-CN.md">简体中文</a>
+</p>
+
+<p>
+  <a href="https://github.com/ECNU-ICALK/SkillGym">GitHub</a> ·
+  <a href="https://huggingface.co/ecnu-icalk/SkillGym-Agent">SkillGym-Agent</a> ·
+  <a href="https://github.com/ECNU-ICALK/SkillGym/blob/main/assets/Internalizing_Large_Scale_Human_Written_Skills_into_LLMs_for_Real_World_Problem_Solving.pdf">Paper</a>
+</p>
 
 </div>
 
 ## Dataset Summary
 
-**SkillGym** is the data release accompanying the SkillGym framework. It connects human-written
-procedural knowledge to executable task environments, code-based outcome verifiers, paired
-with-skill / without-skill executions, and long-horizon agent trajectories.
+**SkillGym** is the data release accompanying the SkillGym framework. It connects human-written procedural knowledge to executable task environments, code-based outcome verifiers, paired with-skill / without-skill executions, and long-horizon agent trajectories.
 
-The dataset is intended to support research on skill-grounded task construction, agent training,
-verifier-based evaluation, procedural transfer, and outcome-based learning.
+The dataset is intended to support research on skill-grounded task construction, agent training, verifier-based evaluation, procedural transfer, and outcome-based learning.
 
 | Statistic | Value |
 | --- | ---: |
@@ -68,11 +79,9 @@ Successful trajectories cover 62 of the 63 sub-categories represented by accepte
 
 ### Release Snapshot
 
-The large-artifact dataset snapshot is associated with GitHub commit
-[`6ebabba`](https://github.com/ECNU-ICALK/SkillGym/commit/6ebabba).
+The large-artifact dataset snapshot is associated with GitHub commit [`6ebabba`](https://github.com/ECNU-ICALK/SkillGym/commit/6ebabba).
 
-The GitHub codebase, dataset repository, and model repository are versioned independently. For exact
-reproduction, record the revision used from each repository.
+The GitHub codebase, dataset repository, and model repository are versioned independently. For exact reproduction, record the revision used from each repository.
 
 ## Quick Start
 
@@ -118,13 +127,10 @@ hf download ecnu-icalk/SkillGym \
   --local-dir .hf/skillgym
 ```
 
-For building new environments, use the
-[SkillGym Task Builder](https://github.com/ECNU-ICALK/SkillGym/tree/main/task_builder).
+For building new environments, use the [SkillGym Task Builder](https://github.com/ECNU-ICALK/SkillGym/tree/main/task_builder).
 
 ## Dataset Organization
 
-At the release level:
-
 ```text
 SkillGym/
 ├── Tasks.tar.zst
@@ -135,26 +141,19 @@ SkillGym/
     └── verifier_passed_*.jsonl
 ```
 
-The task archives contain the source skill/task relationship, task instructions, environment
-configuration, task-specific verification logic, and paired execution variants used during
-construction and validation.
+The task archives contain the source skill/task relationship, task instructions, environment configuration, task-specific verification logic, and paired execution variants used during construction and validation.
 
-Trajectory collections preserve agent interaction traces and verification outcomes for successful
-sampled executions.
+Trajectory collections preserve agent interaction traces and verification outcomes for successful sampled executions.
 
 ## Terminology
 
-The same paired skill-availability concept appears under slightly different names across the paper,
-released data, and implementation:
-
 | Context | Skill available | Skill removed |
 | --- | --- | --- |
 | **Paper / benchmark tables** | `w/ Skills` | `w/o Skills` |
 | **Released dataset variants** | `withskill` | `withoutskill` |
 | **Task Builder internals** | `with_skill` | `no_skill` |
 
-These names refer to the same paired evaluation concept; the differences reflect presentation,
-released-storage, and implementation conventions.
+These names refer to the same paired evaluation concept; the differences reflect presentation, released-storage, and implementation conventions.
 
 ## Task Labels and Paired Variants
 
@@ -170,16 +169,13 @@ A task is labeled **Skill-Dep.** when, under the reference construction configur
 
 ### Verifier-Passed
 
-A **Verifier-Passed** task has a valid executable environment and outcome verifier but does not satisfy
-the stricter contrastive Skill-Dep. criterion under the reference configuration.
+A **Verifier-Passed** task has a valid executable environment and outcome verifier but does not satisfy the stricter contrastive Skill-Dep. criterion under the reference configuration.
 
-These labels characterize accepted environments at construction time. They do **not** guarantee that
-every later model or harness will succeed or fail in the same way.
+These labels characterize accepted environments at construction time. They do **not** guarantee that every later model or harness will succeed or fail in the same way.
 
 ## Trajectory Data
 
-Successful trajectories retain the interaction sequence that led to a verifier-confirmed outcome.
-Depending on the source harness, records may include:
+Successful trajectories retain the interaction sequence that led to a verifier-confirmed outcome. Depending on the source harness, records may include:
 
 - task and session identifiers;
 - task prompt and skill metadata;
@@ -192,17 +188,14 @@ Depending on the source harness, records may include:
 
 ### Schema Note
 
-The eight trajectory files preserve source-specific trace structures. Some nested fields differ across
-harness/model collections, so the release should **not** be assumed to have one perfectly uniform
-nested JSON schema across every file.
+The eight trajectory files preserve source-specific trace structures. Some nested fields differ across harness/model collections, so the release should **not** be assumed to have one perfectly uniform nested JSON schema across every file.
 
 For the most robust loading behavior:
 
 - load one trajectory collection at a time; or
 - explicitly normalize compatible files before concatenating them.
 
-Using an explicit `data_files` path, as in the streaming example above, is preferable to treating every
-trajectory JSONL file as a single homogeneous split.
+Using an explicit `data_files` path, as in the streaming example above, is preferable to treating every trajectory JSONL file as a single homogeneous split.
 
 ## Trajectory Statistics
 
@@ -218,14 +211,10 @@ trajectory JSONL file as a single homogeneous split.
 | Corpus-wide maximum logged text tokens | **342.9k** |
 | Corpus-wide maximum interaction steps | **318** |
 
-The maxima above are corpus-wide maxima for their respective measures and need not come from the same
-trajectory. Logged/saved text-token counts are dataset statistics and should not be interpreted as API
-billing usage or as a single-request context length.
+The maxima above are corpus-wide maxima for their respective measures and need not come from the same trajectory. Logged/saved text-token counts are dataset statistics and should not be interpreted as API billing usage or as a single-request context length.
 
 ## Collection Configurations
 
-Successful trajectories were collected from multiple harness–model configurations:
-
 | Harness | Teacher model | Successful trajectories |
 | --- | --- | ---: |
 | Claude Code | DeepSeek V4 Pro | 1,722 |
@@ -233,9 +222,7 @@ Successful trajectories were collected from multiple harness–model configurati
 | Codex | GPT-5.4 | 1,967 |
 | Codex | Nex-N2-Pro | 2,906 |
 
-This diversity broadens the interaction traces beyond one agent configuration, but the released
-trajectory distribution should not be treated as representative of every model, harness, task domain,
-or deployment setting.
+This diversity broadens the interaction traces beyond one agent configuration, but the released trajectory distribution should not be treated as representative of every model, harness, task domain, or deployment setting.
 
 ## Intended Uses
 
@@ -254,11 +241,9 @@ SkillGym can support research on:
 - Skill-Dep. and Verifier-Passed are reference-configuration labels, not universal properties.
 - Category coverage is broad but uneven.
 - Trajectory collections are generated by a finite set of harness–model configurations.
-- Successful trajectories are a selected subset of sampled executions and should not be interpreted
-  as an unbiased behavioral distribution.
+- Successful trajectories are a selected subset of sampled executions and should not be interpreted as an unbiased behavioral distribution.
 - Some trajectory files preserve harness-specific schema differences.
-- Source skills, fixtures, and supporting assets may carry upstream notices or additional licensing
-  requirements.
+- Source skills, fixtures, and supporting assets may carry upstream notices or additional licensing requirements.
 
 ## Related Resources
 
@@ -271,9 +256,6 @@ SkillGym can support research on:
 
 ## Citation
 
-The accompanying paper is currently under double-blind review. Until final publication metadata is
-available, please use:
-
 ```bibtex
 @misc{skillgym2026,
   title = {Internalizing Large-Scale Human-Written Skills into LLMs for Real-World Problem Solving},
@@ -284,11 +266,8 @@ available, please use:
 
 ## License
 
-The dataset is marked **`license: other`** because it aggregates artifacts that may retain distinct
-upstream notices or additional terms.
+The dataset is marked **`license: other`** because it aggregates artifacts that may retain distinct upstream notices or additional terms.
 
-Repository-level SkillGym code is released under the **MIT License**. The SkillGym-Agent checkpoint
-is released separately under **Apache-2.0**.
+Repository-level SkillGym code is released under the **MIT License**. The SkillGym-Agent checkpoint is released separately under **Apache-2.0**.
 
-When redistributing source skills, fixtures, or supporting assets, preserve the corresponding upstream
-notices and licensing terms.
+When redistributing source skills, fixtures, or supporting assets, preserve the corresponding upstream notices and licensing terms.