SmolDataEnvs: rename, new README, banner

README.md

@@ -3,6 +3,7 @@ license: mit
 task_categories:
 - text-generation
 tags:
+- smoldataenvs
 - sft
 - agent
 - tool-calling
@@ -10,46 +11,77 @@ tags:
 - trl
 ---
 
-# 🛠️ Data Agent — SFT
+<div align="center">
 
-**4,677 worked examples** of an agent doing data science *the right way*. Each row is a complete,
-**verified-correct** trajectory: read the question, poke at the data with a shell tool, reason,
-compute, and write the answer. Every one of these solved its task and passed a deterministic grader
-— so you're fine-tuning on demonstrations that are **known to be correct**, not just plausible.
+<img src="https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-sft/resolve/main/banner.png" alt="SmolDataEnvs" width="100%">
 
-Drop-in ready for [TRL](https://github.com/huggingface/trl): conversational `messages` + `tools`.
+# 🛠️ SmolDataEnvs — SFT
+
+**5.5K+ RL tasks for hill-climbing small models in code and data science.**
+
+[![Collection](https://img.shields.io/badge/%F0%9F%A4%97%20Collection-SmolDataEnvs-FFD21E?style=for-the-badge&labelColor=1a1a1a)](https://huggingface.co/collections/FineEnvs/smoldataenvs)
+
+</div>
+
+
+**4,677 worked examples** of an agent doing data science the right way. Each row is a complete,
+verified-correct trajectory: read the question, poke at the data with a shell tool, reason, compute,
+write the answer. Every one of them solved its task and passed the deterministic grader, so you are
+fine-tuning on demonstrations that are known to be correct rather than merely plausible.
+
+Drop-in ready for [TRL](https://github.com/huggingface/trl): conversational `messages` plus `tools`.
 
-## Where it comes from
-These are real agent rollouts on the [Data Agent](https://huggingface.co/HuggingEnvs) tasks, which
-were themselves built from the [**jupyter-agent dataset**](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
-(data-science notebooks over Kaggle datasets). We kept **only trajectories that reached the correct
-answer** under deterministic grading (reward = 1.0) — one clean demonstration per task.
 
 ## What's inside
-- **4,677 correct trajectories** — one per task
+
+- **4,677 correct trajectories**, one per task
 - **Difficulty** — easy 1,402 · medium 2,640 · hard 635
-- One tool throughout: `bash` (shell command execution)
+- One tool throughout: `bash`
 
 ## What's in a row
+
 - **`messages`** — the full conversation in OpenAI/TRL chat format: `system` → `user` (the task) →
-  `assistant` (reasoning + `tool_calls`) → `tool` (command output) → … → final `assistant` answer.
-  Tool-call `arguments` are JSON objects; `tool` messages carry the tool `name`.
-- **`tools`** — the `bash` tool's JSON schema (rendered by `apply_chat_template(..., tools=...)`)
+  `assistant` (reasoning plus `tool_calls`) → `tool` (command output) → … → final `assistant` answer
+- **`tools`** — the `bash` tool's JSON schema, for `apply_chat_template(..., tools=...)`
 - `task_id`, `difficulty` (1–5), `difficulty_tier`, `n_turns`, `source_agent`
 
 ## Fine-tune with TRL
+
 ```python
 from datasets import load_dataset
-from trl import SFTTrainer, SFTConfig
-
-ds = load_dataset("HuggingEnvs/data-agent-sft", split="train")
+from trl import SFTConfig, SFTTrainer
 
+ds = load_dataset("FineEnvs/SmolDataEnvs-sft", split="train")
 trainer = SFTTrainer(
-    model="Qwen/Qwen2.5-3B-Instruct",     # any tool-capable chat template
-    train_dataset=ds,                      # messages + tools are picked up automatically
-    args=SFTConfig(assistant_only_loss=True, max_length=8192),
+    model="HuggingFaceTB/SmolLM3-3B",
+    train_dataset=ds,
+    args=SFTConfig(output_dir="smoldataenvs-sft", max_length=8192),
 )
 trainer.train()
 ```
-The `messages` + `tools` columns render through your model's chat template, and
-`assistant_only_loss=True` trains on the assistant's tokens only — no dataset wrangling needed.
+
+A runnable notebook and a single-file script for HF Jobs are in
+[FineEnvs/04-smoldataenvs](https://github.com/adithya-s-k/FineEnvs/tree/main/04-smoldataenvs).
+
+## Where it comes from
+
+Built from the [jupyter-agent dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
+— real data-science notebooks over 471 Kaggle datasets. Every question–answer pair was extracted and
+then **verified**: strong agent models had to solve the task in a live sandbox and reproduce the gold
+answer under deterministic grading. Anything ambiguous or un-checkable was dropped. So every task
+here is known-solvable and unambiguously gradable.
+
+**Verified by a checker, not judged by a model.** Grading is an exact comparison against a known
+answer, through a ladder of checks: exact match → numeric with tolerances → list and percent
+normalisation → symbolic equivalence. No LLM sits in the reward path, so the signal does not drift
+when you change the grader's model, because there isn't one.
+
+## The family
+
+| Repo | What it is |
+|---|---|
+| [`SmolDataEnvs`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs) | the tasks as plain rows �� load it and prompt any model |
+| [`SmolDataEnvs-sft`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-sft) | 4,677 verified agent trajectories, TRL-ready |
+| [`SmolDataEnvs-harbor-train`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train) | 5,000 tasks as Harbor environments |
+| [`SmolDataEnvs-harbor-test`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-test) | 250 held-out, deliberately harder |
+| [`SmolDataEnvs-harbor-eval`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-eval) | 144 for quick validation during a run |