---
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.