| --- |
| license: apache-2.0 |
| language: [en] |
| tags: [reinforcement-learning, data-science, code-agent, jupyter, harbor, curriculum, difficulty] |
| size_categories: [1K<n<10K] |
| --- |
| |
| # AdithyaSK/data_agent_rl_environment_train_difficulty_ranked |
|
|
| A **Harbor task suite** of 2238 data-agent tasks, **ordered easy -> hard** by *empirical* |
| difficulty measured from a pass@4 rollout sweep (Qwen3.5-4B + 2B, bash harness). |
|
|
| ## Layout (standard Harbor spec) |
| ``` |
| tasks/<task_id>/{task.toml, instruction.md, environment/, tests/} |
| registry.json # tasks[] IN DIFFICULTY ORDER (rank 1 = easiest); each entry has rank/difficulty/solve_frac |
| manifest.json # full ranked table (all signals + task metadata) |
| ``` |
|
|
| ## The ordering |
| `registry.json`'s `tasks[]` array is sorted easy -> hard. Each entry: |
| `{name, path, rank, difficulty, solve_frac, mean_tool_calls}`. |
|
|
| `difficulty` (0 easiest .. 1 hardest) = `0.70·(1-solve_frac) + 0.12·tool_calls + 0.10·tokens + 0.08·time`, |
| from the real rollouts. Validated against the earlier LLM difficulty labels (monotonic L1->L5). |
|
|
| ## Tasks |
| Same 2238 tasks as [`AdithyaSK/data_agent_rl_environment_train`](https://huggingface.co/datasets/AdithyaSK/data_agent_rl_environment_train), |
| but each carries the **base image** (`savatar101/env-data-agent-train:base`) and the **3-reward |
| verifier** (`correctness`, `submission`, `tool_efficiency` -> `reward.json`). Curriculum-ready. |
|
|
