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ALFWorld

TextWorld household tasks: the agent is told “put a clean mug in the cabinet” and has to act through a text interface — go to, take, heat, put — until the game says the goal is reached. Solved means the goal was reached within 50 actions; there is no partial credit per task.

Study on 100 ids from the official train split; each pool below is scored once per checkpoint. valid_unseen is ALFWorld’s own out-of-distribution evaluation split; valid_seen and valid_train are its two other evaluation splits.

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Last export 2026-10-19T17:07:34Z · 8 methods · 171 published points, each recomputable from its per-task verdicts.

train · 100 tasks · H0 0.530 · study split

Section titled “train · 100 tasks · H0 0.530 · study split”
0.08 0.31 0.54 0.77 1.00 r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 H0 (round 0) 0.530 sica_ci ahe dgm harnessx hyperagents tthe dream_rsi meta_harness checkpoint round

Score — study split, 100 tasks per round

methodr0 (H0)r1r2r3r4r5r6r7r8r9r10record
sica_ci0.5300.8900.9301.0001.0001.0001.0001.0001.0001.0001.000↗
ahe0.5300.9201.0001.000———————↗
dgm0.5300.8200.8900.9400.9500.950—————↗
harnessx0.5300.7100.7900.9100.9700.9701.0001.0001.0001.0001.000↗
hyperagents0.5300.7400.9000.990———————↗
tthe0.5300.6500.6700.7000.7000.7400.7400.8400.7600.8300.930↗
dream_rsi0.5300.7600.8100.9901.0001.0001.0000.600———↗
meta_harness0.5300.2500.6100.4900.6100.6700.1600.6900.7000.660—↗

The r0 (H0) column is one run — the same harness on the same tasks — not a constant: its record holds the trajectories and the per-task verdicts.

Cost — task-agent: millions of tokens the harness spent acting, per round

task-agent tokens per round — millions of tokens (M); in = uncached prompt tokens, out = generated tokens
methodr1r2r3r4r5r6r7r8r9r10
inoutinoutinoutinoutinoutinoutinoutinoutinoutinout
sica_ci1.180.010.130.00reuse—reuse—reuse—reuse—reuse—reuse—reuse—reuse—
ahe17.640.063.560.033.560.03——————————————
dgm15.930.0613.310.059.790.0510.570.043.710.02——————————
harnessx23.050.0820.150.0712.830.059.580.059.040.042.370.022.350.022.180.022.170.022.180.02
hyperagents19.130.0719.900.090.280.00——————————————
tthe14.780.0617.510.0722.680.0812.840.0715.580.0715.530.0715.430.0719.750.0716.090.0713.630.06
dream_rsi19.500.0718.040.060.040.00reuse—reuse—reuse—18.190.07——————
meta_harness11.600.0612.670.056.030.0512.950.0627.720.105.810.1126.520.1020.750.0827.960.10——

Cost — improver: millions of tokens the method spent editing, per round

improver tokens per round — millions of tokens (M); in = uncached prompt, cache = prompt read from cache, out = generated
methodr1r2r3r4r5r6r7r8r9r10
incacheoutincacheoutincacheoutincacheoutincacheoutincacheoutincacheoutincacheoutincacheoutincacheout
sica_ci0.048.70.080.0411.30.070.118.20.070.0511.20.080.058.40.070.0214.20.070.0517.40.080.0720.40.080.0825.00.080.0413.70.08
ahe0.1029.30.180.0924.80.16————————————————————————
dgm0.1519.00.15———0.1851.20.27—————————————————————
harnessx0.039.50.080.0410.40.080.139.50.070.0614.10.070.0311.10.070.0514.20.080.128.70.070.0511.00.070.0915.80.080.1612.10.07
hyperagents0.1129.70.23———————————————————————————
tthe0.0810.40.070.049.50.080.126.10.050.0617.20.070.0610.90.080.0510.90.070.0715.50.080.0410.90.080.1011.80.080.0419.40.09
dream_rsi0.0311.10.080.0614.10.090.0412.60.070.139.70.060.0310.90.07——————0.0210.10.09——————
meta_harness0.155.00.08————————————————————————0.2117.80.18

valid_unseen · 134 tasks · H0 0.403 · 57 points

Section titled “valid_unseen · 134 tasks · H0 0.403 · 57 points”
0.01 0.26 0.50 0.75 1.00 r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 H0 (round 0) 0.403 sica_ci ahe dgm harnessx hyperagents tthe dream_rsi meta_harness checkpoint round

Score — valid_unseen, 134 tasks per point

methodr0 (H0)r1r2r3r4r5r6r7r8r9r10record
sica_ci0.4030.9700.8961.0001.0001.0001.0001.0001.0001.0001.000†↗
ahe0.4030.9931.0001.000†———————↗
dgm0.4030.8510.8430.9630.9780.985†—————↗
harnessx0.4030.5900.7010.9400.9700.9701.0001.0001.0001.0001.000†↗
hyperagents0.4030.6871.0001.000†———————↗
tthe0.4030.5600.6270.6190.5820.6790.7160.8660.7760.9100.933†↗
dream_rsi0.4030.6190.7911.0001.0001.0001.000†0.478———↗
meta_harness0.4030.2310.4780.3660.4850.5970.0970.5900.545†0.590—↗

Cost — task-agent: millions of tokens per measured point (an evaluation point runs no improver, so there is no improver column here)

task-agent tokens per measured point — millions of tokens (M); one point is one measurement, so there is no per-token split here
methodr1 (M)r2 (M)r3 (M)r4 (M)r5 (M)r6 (M)r7 (M)r8 (M)r9 (M)r10 (M)
sica_ci0.82.2reusereusereusereusereusereusereusereuse
ahe8.64.24.2———————
dgm19.919.810.213.22.3—————
harnessx33.631.113.411.311.74.24.03.93.83.8
hyperagents28.812.40.2———————
tthe18.026.435.113.818.218.019.928.019.316.4
dream_rsi29.622.1reusereusereusereuse24.8———
meta_harness13.916.48.316.635.77.436.029.037.3—

valid_seen · 140 tasks · H0 0.414 · 57 points

Section titled “valid_seen · 140 tasks · H0 0.414 · 57 points”
0.05 0.29 0.52 0.76 1.00 r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 H0 (round 0) 0.414 sica_ci ahe dgm harnessx hyperagents tthe dream_rsi meta_harness checkpoint round

Score — valid_seen, 140 tasks per point

methodr0 (H0)r1r2r3r4r5r6r7r8r9r10record
sica_ci0.4140.9360.8861.0001.0001.0001.0001.0001.0001.0001.000↗
ahe0.4140.8931.0001.000†———————↗
dgm0.4140.8140.8290.9290.9570.971—————↗
harnessx0.4140.5570.6430.8500.9710.9711.0001.0001.0001.0001.000↗
hyperagents0.4140.6290.9141.000†———————↗
tthe0.4140.5930.6000.6790.6710.7070.7000.8210.8000.8710.914↗
dream_rsi0.4140.7140.8641.0001.0001.0001.0000.550———↗
meta_harness0.4140.1360.4710.4210.5070.5930.1860.6430.5790.657—↗

Cost — task-agent: millions of tokens per measured point (an evaluation point runs no improver, so there is no improver column here)

task-agent tokens per measured point — millions of tokens (M); one point is one measurement, so there is no per-token split here
methodr1 (M)r2 (M)r3 (M)r4 (M)r5 (M)r6 (M)r7 (M)r8 (M)r9 (M)r10 (M)
sica_ci1.00.7reusereusereusereusereusereusereusereuse
ahe25.93.43.4———————
dgm22.122.113.415.01.8—————
harnessx38.134.620.613.513.13.43.43.33.23.3
hyperagents31.425.30.4———————
tthe21.529.633.018.922.722.522.925.821.618.2
dream_rsi28.120.40.0reusereusereuse25.1———
meta_harness16.621.58.220.041.77.338.633.441.2—

valid_train · 200 tasks · H0 0.525 · 57 points

Section titled “valid_train · 200 tasks · H0 0.525 · 57 points”
0.07 0.30 0.54 0.77 1.00 r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 H0 (round 0) 0.525 sica_ci ahe dgm harnessx hyperagents tthe dream_rsi meta_harness checkpoint round

Score — valid_train, 200 tasks per point

methodr0 (H0)r1r2r3r4r5r6r7r8r9r10record
sica_ci0.5250.9400.9301.0001.0001.0001.0001.0001.0001.0001.000↗
ahe0.5250.9051.0001.000———————↗
dgm0.5250.7900.8400.9600.9300.950—————↗
harnessx0.5250.6200.6550.8450.9550.9651.0001.0001.0001.0001.000↗
hyperagents0.5250.7050.9400.995———————↗
tthe0.5250.5900.6300.6800.6300.6450.6800.7550.7350.8100.890↗
dream_rsi0.5250.6600.8150.9901.0001.0001.0000.480———↗
meta_harness0.5250.1900.5800.4850.5850.6450.1550.6650.6350.610—↗

Cost — task-agent: millions of tokens per measured point (an evaluation point runs no improver, so there is no improver column here)

task-agent tokens per measured point — millions of tokens (M); one point is one measurement, so there is no per-token split here
methodr1 (M)r2 (M)r3 (M)r4 (M)r5 (M)r6 (M)r7 (M)r8 (M)r9 (M)r10 (M)
sica_ci1.90.8reusereusereusereusereusereusereusereuse
ahe32.36.36.3———————
dgm32.230.717.622.45.9—————
harnessx47.446.227.819.419.36.56.66.26.16.1
hyperagents40.029.10.1———————
tthe32.542.645.624.632.030.933.840.231.026.7
dream_rsi41.330.40.1reusereusereuse38.8———
meta_harness24.128.211.728.353.510.752.943.958.2—
  • study: 100 ids from train (train100.csv), 10 rounds, whole set per round
  • measure: every saved round on each split. The budget is 48 machine-wide slots; --jobs is how many of them one invocation may use. The checkpoint sweeps ran two shards at --jobs 24, which is what keeps the two cards balanced: one call-level balancer in front of both servers, so every model call goes to whichever card is free.
  • score: the environment’s own verifier; per-task verdicts are kept, so a score recomputes from per_task
Terminal window
# study
HG_ALFWORLD_STUDY_IDS=train100.csv HG_ALFWORLD_POOL=valid_unseen \
python3 driver.py --harness loop --dataset alfworld --split declared --side train \
--rounds 10 --trials 1 --jobs 24 --improver dsh \
--method-entrypoint 'python3 methods/<method>/run.py' --run-id <id> --runs-root runs
# one saved checkpoint on one split
HG_ALFWORLD_STUDY_IDS=train100.csv HG_ALFWORLD_POOL=valid_unseen \
python3 driver.py --harness loop --dataset alfworld --split declared --side eval \
--from-run <id> --from-round <N> --trials 1 --jobs 24 --improver none \
--run-id <id>-ckpt-r<N>-valid_unseen --runs-root runs

Published on HuggingFace; until the repository is announced, ask in an issue.

Bring your own setup — a different base harness, model or improver is welcome as long as it is declared and H0 is measured on it. Format and verification: Contributing.