Dataset Viewer
Auto-converted to Parquet Duplicate
task_id
stringlengths
6
11
domain
stringclasses
7 values
prompt
stringlengths
927
3.71k
finance-18
finance
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Set up a discount curve and value a set of instruments. The file quotes.csv contains the columns ty...
math-1
math
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Answer every exact-integer problem listed below. Because the values are too large for a naive brute...
media-12
media
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. stream.mcf is a corrupted media container. Its structure consists of the magic bytes 'MCF1' followe...
office-3
office
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. The team roster is located in roster.csv (columns: full_name, aliases separated by pipes), and meet...
science-12
science
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Combine the chemical kinetics model found in params.json (containing rate constants k1,k2,k3) with ...
security-21
security
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Audit a synthetic firewall ruleset. The file rules.csv has columns id,action,proto,src,dst,dport,en...
software-17
software
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Create `kv.py` (stub) containing `TxError(Exception)` and a `Store` class implementing an in-memory...
finance-21
finance
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Determine the contents of credit-card statements. The file transactions.csv contains the columns da...
math-3
math
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Resolve the following computational problems precisely (python3 and numpy are available; Python int...
media-13
media
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. The file stream.mcf is a corrupted media container. Its structure begins with the magic bytes 'MCF1...
office-14
office
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Perform intercompany ledger reconciliation for the corporate group with a cutoff of 2026-09-30. ent...
science-13
science
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Using the 4-species kinetics defined in params.json (k1,k2,k3,kf,kr, dose, t_dose), begin integrati...
software-23
software
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Create `cronlib.py` (stub): define `next_times(expr, after, count) -> list[str]`, which acts as a c...
math-7
math
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. The file roads.csv contains a road network where lines beginning with # are comments, and if a pair...
media-19
media
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. playlist.m3u8 is a synthetic HLS media playlist. Determine its timeline structure. The rules are as...
office-15
office
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Perform reconciliation of the intercompany ledgers for a corporate group with a cutoff date of 2026...
science-16
science
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. The file dist.csv contains a symmetric 13x13 matrix of estimated pairwise evolutionary distances, w...
software-27
software
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Create `interp.py` (stub) defining `InterpError(Exception)` and `interpolate(text, env) -> str`, wh...
math-14
math
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. The alphabet consists of {a,b,c}. The file params.json specifies the queries (lengths n) and the fo...
office-17
office
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Create a 14-day schedule commencing on Monday 2026-11-02 (weeks run Mon-Sun, meaning days 1-7 and 8...
science-20
science
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Identify the earthquake source using the provided phase picks. The file stations.csv provides colum...
math-17
math
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. Facility location problem. The file depots.csv has columns (id,fixed,cap); activating a depot incur...
office-22
office
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. sheet.csv is a compact spreadsheet consisting of 5 columns (A to E) and 9 rows without a header: li...
office-23
office
Operate from the current directory by executing shell commands; the environment provides python3 with access to numpy, pandas and Pillow. Once completed, respond with {"answer": "done"} to trigger scoring of your files. The file sheet.csv is a compact spreadsheet consisting of 12 rows and 5 columns (A through E) witho...

Mixed Skills v6: eight-domain verified workspace tasks

One OpenEnv server, 190 deterministic procedurally generated tasks across the eight evaluation domains: software engineering, industrial and physical systems, natural science, office work, finance and economics, math and formal reasoning, cybersecurity, and media production. Each task gives the agent a scratch workspace with input files and a written specification. The agent works with shell commands and is scored by a hidden verifier on the files it leaves behind.

tasks.jsonl lists every task id with its domain and the exact first-turn prompt. The image is docker.io/chrismun/mixed-env:v6 (linux/amd64); it contains 190 task ids and the submission names the 24 used for training.

Interaction

Each turn the model replies with exactly one JSON object:

  • {"command": "<shell command>"} runs in the workspace (python3 with numpy, pandas and Pillow). At most 12 commands per episode; each command's output is truncated to its last 1500 characters, so strong solutions inspect with head and do the work in a script.
  • {"answer": "done"} ends the episode and scores the workspace.

Observations carry the command output, the commands left and. There is no reminder text. Replies that are not a single JSON object end the episode, which is then scored on the workspace as it stands.

Reward

Terminal and in [0, 1]. Every verifier is a weighted set of independent sub-checks recomputed from the task inputs, so partial progress earns partial reward and only a fully correct deliverable scores 1.0. References live only inside the image, agent commands run as an unprivileged user with memory and file-size limits, and verifiers that execute agent code do so in a subprocess with a timeout. An untouched workspace scores at most 0.25 on every task.

Calibration

Tasks were measured with Qwen3.8-27B (thinking off) inside a tool-calling GRPO training harness, because pass rates there differ a lot from a strict JSON-reply driver (the two correlate only 0.25 per task). Each task was sampled 8 to 56 times across profiling and training passes. The 24 training tasks are those whose pooled pass rate (shrunk toward 0.5) falls between 0.08 and 0.45.

  • Pooled mean pass@1 of the 24 tasks: about 0.33, with the hardest near 0.1 (science and finance) and the easiest near 0.45. Selecting on low observed rates regresses upward, so the true start is probably a little higher.
  • Every task runs bare: no reply-format reminder and no per-step hint.
  • Prompt text: the task statements and the three global strings were rewritten by Qwen3.8-27B from the original statements under strict gates (every number, file name, identifier, quoted string and JSON example must survive verbatim). Difficulty of the rewrites was not re-matched task by task.
  • No transcript in the profiling runs touched verifier or task source.

Domains

Domain Skills exercised
software multi-file bug fixing against hidden tests, implementing modules from a spec, CLI conversion, migration scripts, simulator debugging, performance and dependency problems
industrial sensor log analysis and fault reports, control tuning by simulation, pipe and pump sizing, tariff billing, inventory policy, flow-line simulation
science curve and rate fitting, sequence analysis and protein mass, ODE integration, hypothesis tests, titration equilibria, spectral analysis
office payables reconciliation, meeting-minutes extraction, room scheduling, expense audits, payroll, contact deduplication
finance amortization, market-data analytics, ledger audit, bond pricing, tax lots, FX arbitrage
math exact number theory, proofs by computation, assignment optimization, shortest routes, constructive codes, absorbing Markov chains, impartial games
security log incident analysis, hardening vulnerable code, layered-encoding and crypto forensics, detection rules, advisory matching (all synthetic, defensive)
media subtitle retiming, image pipelines, audio synthesis, SVG charts, timecode conformance, markdown to RSS

Files

  • tasks.jsonl: task_id, domain, prompt for each of the 24 training tasks, in training order (domains interleaved).
Downloads last month
77