Datasets:
ECHO has no maze, so I built one: the "free world model" loss is inert in pure imitation
Rig: one RTX 5090 32GB · torch 2.10+cu128 (sm_120) · a 10M-param decoder-only transformer trained from scratch (minutes per run, ~5.7GB peak, runs alongside a live 27B server)
Paper: ECHO — Terminal Agents Learn World Models for Free (arXiv 2605.24517, code microsoft/echo-rl). The idea: standard agent RL (GRPO) trains only on action tokens and masks the environment output; ECHO adds a free cross-entropy on those env-observation tokens (already in the rollout, same forward pass) so the policy learns an implicit world model. world_model_coeff=0.0 recovers vanilla GRPO; >0 is ECHO.
First, a correction: the paper and repo contain no maze microcosm — I grepped both (maze=0, microcosm=0, toy=0). ECHO is exclusively a full-scale terminal-agent RL paper (Qwen3-8B/14B). So this isn't a reproduction — it's a microcosm I designed to isolate ECHO's mechanism on hardware anyone can run, before committing GPU-weeks to the real thing.
The setup
A 10M transformer, behavior-cloned on BFS-optimal maze trajectories encoded as a token "terminal": interleaved observation tokens (the 4-neighbour wall pattern + a coarse goal bearing) and the optimal action. The entire A/B is the loss mask: action-only (λ=0) computes loss on action tokens only; ECHO-style (λ=1) adds the free CE on the observation tokens too. Same data, same model, same forward pass.
Eval: greedy rollout, the environment supplies the true next observation (the model's predicted obs is training-only), solve = reach goal within 4x the optimal path. 300 held-out mazes per size, 3 seeds, sizes 6x6 / 7x7 / 8x8. Then the decisive ablation — walls-only, dropping the goal bearing so the agent can't just follow a handed-over direction and must build an internal map (exactly where predicting walls should help).
The numbers
Solve rate, 3-seed mean ± spread. Gap = (λ1 − λ0) in points.
| regime | 6x6 | 7x7 | 8x8 |
|---|---|---|---|
| full obs action-only | 0.788 ± 0.017 | 0.713 ± 0.009 | 0.633 ± 0.012 |
| full obs + env-token | 0.807 ± 0.021 | 0.706 ± 0.006 | 0.623 ± 0.022 |
| → gap | +1.9 | −0.8 | −1.0 |
| walls-only action-only | 0.606 ± 0.010 | 0.492 ± 0.011 | 0.491 ± 0.012 |
| walls-only + env-token | 0.622 ± 0.011 | 0.502 ± 0.018 | 0.482 ± 0.011 |
| → gap | +1.7 | +1.0 | −0.9 |
A clean null, twice. Every gap sits inside the seed spread, with no consistent sign and — the tell that matters — no growth with maze size in either regime. The hypothesis was that a world model should help more as mazes get bigger; the opposite of that is "no trend," and that's what we get. The walls-only ablation kills the obvious objection: even when the agent is denied the goal direction and has to map the maze itself, predicting observations buys nothing.
Why it's inert (and what it says about ECHO)
In behavior cloning the policy already gets a clean, dense supervision signal — the optimal action at every step. Predicting the next observation is a representation-learning side-quest that doesn't change the action decision the model is being graded on. The world-model tokens are "free" to add, and free is exactly what they're worth here.
This doesn't refute ECHO — it locates its gain. ECHO is on-policy RL, where the reward signal is sparse and the rollouts are the model's own. In that setting the env-token loss can shape the rollout distribution and credit assignment — it has a job to do that simply doesn't exist in imitation. So the lesson the microcosm actually teaches: the "for free" gain is not a generic auxiliary-loss free lunch you can bolt onto any objective. It lives in the RL coupling. Which is the whole point of testing it on-policy next.
Honest caveats
- This is behavior cloning, not RL — by design. It tests whether the auxiliary loss alone helps; it cannot speak to the on-policy regime ECHO actually uses.
- 10M from scratch, not an 8–14B pretrained LLM. A microcosm isolates a mechanism; it doesn't transfer magnitudes.
- The +1.7 to +1.9 pt at 6x6 is within seed spread — reported, not claimed.
- One λ (0 vs 1) and one architecture. The point was a clean mechanism read, not a sweep.
Reproduce
lib/echo_maze/ (maze + BFS, partial-obs env, encoding, the loss-mask A/B, the 10M GPT, BC training, rollout) + scripts/echo_maze_sweep.py (OBS_MODE=walls_only for the ablation) + scripts/chart_echo_maze.py. 15 unit tests (stdlib logic on CPU, torch on GPU). Two sweeps, ~35 min each on one 5090.