---
license: apache-2.0
language: [en]
tags: [reinforcement-learning, data-science, code-agent, jupyter, harbor, curriculum, difficulty]
size_categories: [1K<n<10K]
configs:
- config_name: default
  data_files: "*.parquet"
---

# AdithyaSK/data_agent_rl_environment_train_difficulty_ranked

**Empirical difficulty ranking** of the 2238 tasks in
[`AdithyaSK/data_agent_rl_environment_train`](https://huggingface.co/datasets/AdithyaSK/data_agent_rl_environment_train),
ordered **easy → hard** (`rank` 1 = easiest).

Derived from a **pass@4 rollout sweep** (Qwen3.5-4B + Qwen3.5-2B, bash harness, 4 attempts each,
up to 8 attempts/task) — ranked from what the models *actually did*, not an LLM's guess.

## Columns
| column | meaning |
|---|---|
| `rank` | 1 = easiest … 2238 = hardest |
| `difficulty` | composite 0..1 (0 easiest). `0.70·(1-solve_frac) + 0.12·calls + 0.10·tokens + 0.08·time` |
| `solve_frac` | fraction of all attempts that passed (primary signal) |
| `pass4_4b`, `pass4_2b` | did the model solve it in ≥1 of its 4 attempts (0/1) |
| `mean_calls` | avg bash tool calls per attempt |
| `mean_elapsed` | avg wall-clock seconds per attempt |
| `med_tokens` | median completion tokens per attempt |
| `no_answer_frac` | fraction of attempts that never submitted |
| `question`, `kaggle_dataset_name`, `reward_mode_initial` | from the task |
| `difficulty_level` | the earlier **LLM-assigned** label (L1–L5) — for comparison |

## Validation
Empirical difficulty rises monotonically with the LLM-assigned `difficulty_level`
(L1≈0.21 → L5≈0.84), confirming the ranking is sound while adding fine-grained ordering
*within* each level (solve rate + tool calls + time + tokens).

## Use
Curriculum RL (train easy→hard), difficulty-stratified eval, or filtering to the
partial-solve band (`0 < solve_frac < 1`) which carries the most GRPO signal.