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craftax ReMDM planner — RL fine-tuning ablations (temporary)
Merged results of the 25-arm RL fine-tuning ablation suite, 3 seeds per arm.
| Date | 2026-08-21 |
| Repo | craftax-ReMDM-planner @ 9a45363 |
| Config | experiments/rl_finetuning/configs/ablations_final_craftax_classic_ucl.yaml |
| Env | Craftax-Classic-Symbolic-v1 |
| Checkpoint | W&B myopic-planner/craftax-ReMDM-planner/8qw13bmd -> artifact Craftax-Classic-Symbolic-v1-policy-best:v2 (Orbax step 40370176) |
| Hardware | 3x RTX 3090 Ti (UCL: shoveler-l, smew-l, wigeon-l) |
| Seeds | 3 per arm |
Produced by run_ablations.py --merge over three per-GPU shards; all three
shards ran one config, so --merge pooled them without refusal.
Contents: results.json, diagnosis.md, 113 figures, 19 tables.
Temporary staging copy — not a release artefact.
pretrained_checkpoint/
The exact pretrained planner these 25 ablations fine-tune from.
| W&B run | myopic-planner/craftax-ReMDM-planner/8qw13bmd |
| Artifact | Craftax-Classic-Symbolic-v1-policy-best:v2 (best-validation, not final) |
| Format | Orbax checkpoint, step 40370176 |
| Size | 34 MB, 13 files |
Restores with src/planners/model.restore_latest. The sibling artifact
Craftax-Classic-Symbolic-v1-policy:v3 is the final-step checkpoint of the
same run and is not what these ablations used.
inference/
--mode inference evaluation of the bundled checkpoint, run 2026-08-21 on a
UCL RTX 3090 Ti (shoveler-l).
| Command | --mode inference --config configs/final_craftax_classic_ucl.yaml --override use_wandb=false |
| Protocol | 32 agents x 10,000 steps, seed 42, diffusion_steps_eval 10 |
| mean_score | 3.256 (best 8.100) |
| mean episode length | 161.2 |
Top achievement rates: collect_wood 0.97, collect_sapling 0.94,
place_table 0.81, place_plant 0.50. Zero: eat_plant,
defeat_skeleton, make_stone_sword.
Not comparable to results.json's pretrained_score
results.json records pretrained_score ~11.72 for this same checkpoint.
The two numbers come from different samplers, not different settings:
--mode inference |
ablation suite eval | |
|---|---|---|
| sampler | sample_plan_inpainting |
sample_plan |
| replan cadence | every env step | every EVAL_REPLAN = 8 steps |
| actions per plan | 1 (history-conditioned) | 8 |
| denoising steps | DIFFUSION_STEPS_EVAL = 10 |
VAL_DIFFUSION_STEPS = 50 |
The denoising budget is not the cause: re-running inference with
--override diffusion_steps_eval=50 gives 3.4 against 3.3 at 10 steps, a ~3%
move against a 3.6x gap. The sampler and replan cadence account for the rest.
offline_bc_checkpoint/ + inference/craftax_offline_bc_final.json
An offline BC planner trained 2026-08-22, distinct from the DAgger checkpoint above. Added so this repo carries both arms of the comparison.
| W&B run | myopic-planner/craftax-ReMDM-planner/4oeqk0yl (…-Offline-Diffusion-BC-99M) |
| Training | --mode offline --config configs/final_craftax_classic_ucl.yaml, seed 42 |
| Expert | Craftax-Classic-Symbolic-v1-PPO_RNN-1000M (rolled out live; --mode offline consumes no dataset) |
| Budget | 99,942,400 env frames = 1525 updates x 512 envs x 128 steps; 97,600 gradient steps |
| Wall clock | 56,283 s (~15.6 h) on a UCL RTX 3090 Ti (shoveler-l), SPS 1778 |
| Format | Orbax, step 99942400 (98 MB) |
| Also on W&B as | Craftax-Classic-Symbolic-v1-policy:v4 |
Pass the directory, not the step subdirectory — CheckpointManager resolves
the step itself.
Inference vs the DAgger checkpoint
Identical protocol both sides: 32 agents x 10,000 steps, seed 42,
diffusion_steps_eval 10.
| metric | offline BC | DAgger policy-best:v2 |
|---|---|---|
| mean_score | 3.8812 | 3.2562 |
| best_score | 7.1000 | 8.1000 |
| mean_episode_length | 139.59 | 161.16 |
Offline BC scores +0.625 mean over DAgger, though its single best episode is lower (7.10 vs 8.10) — it is more consistent rather than higher-ceilinged.
Achievement rates where the two differ:
| achievement | offline BC | DAgger | delta |
|---|---|---|---|
make_wood_pickaxe |
0.406 | 0.188 | +0.219 |
make_wood_sword |
0.156 | 0.031 | +0.125 |
defeat_zombie |
0.125 | 0.031 | +0.094 |
make_stone_sword |
0.094 | 0.000 | +0.094 |
collect_sapling |
1.000 | 0.938 | +0.062 |
place_table |
0.875 | 0.812 | +0.062 |
place_plant |
0.562 | 0.500 | +0.062 |
collect_stone |
0.219 | 0.156 | +0.062 |
collect_drink |
0.125 | 0.062 | +0.062 |
make_stone_pickaxe |
0.000 | 0.094 | -0.094 |
eat_cow |
0.062 | 0.125 | -0.062 |
place_furnace |
0.062 | 0.094 | -0.031 |
place_stone |
0.031 | 0.062 | -0.031 |
The gain is concentrated in the early wood/stone tech tree. make_stone_pickaxe
is the one clear regression, and it is odd next to make_stone_sword improving
— at 32 agents each achievement is 32 Bernoulli trials, so a 0.094 difference is
3 agents and well inside sampling noise.
Caveat carried from the section above
These numbers use --mode inference's sample_plan_inpainting sampler and are
not comparable to results.json's pretrained_score scale.
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