Datasets:

AdithyaSK
/

data_agent_rl_environment_train_difficulty_ranked

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, 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.