Know what deterministic inference fixes

We turned on deterministic inference to close the gap between the trainer and the engine. The gap did not move. What changed was that two runs with the same seed now gave the same rollout at step 0.

When you finish this section, you can decide when deterministic inference is worth its cost.

Our result: with and without deterministic inference, the median gap stayed between 0.0151 and 0.0153. Two deterministic runs repeated the rollout of step 0 exactly.

Turn it on

Add the engine flag and an attention backend that supports it to the run of section 1.1, both through MILES_EXTRA_ARGS:

-e MILES_EXTRA_ARGS="--sglang-enable-deterministic-inference --sglang-attention-backend triton"

The launcher also reads MILES_SGLANG_ATTENTION, but only in true on-policy mode with MILES_TRUE_ON_POLICY=1. In a normal run, that variable changes nothing.

On our GB10, both triton and flashinfer started with the flag. The engine also switches its sampler, and the log says so:

[2026-10-21 13:45:36] Sampling backend is set to pytorch for deterministic inference.
[2026-10-21 14:55:18] Sampling backend is set to pytorch for deterministic inference.

The gap stays the same

We ran four 12-step runs with seed 1 on Qwen3-0.6B:

We measured the same gap with and without deterministic inference. The four medians are within 0.0002 of each other. Raw runs

This points to the kernels as the cause: the two sides compute with different kernels. Deterministic inference makes the engine give the same result for the same input, whatever the batch around it. It does not make the engine compute like the trainer.

The first step repeats

The two Triton runs gave the same gap at step 0, to six decimal places: 0.015404. From step 1 they drifted apart.

The engine repeated itself, and the trainer most likely did not. These runs did not set the trainer's deterministic mode, so the weights after the first update can differ between the runs. We have not tested that with the trainer flags on.

So use deterministic inference when you need to repeat an experiment, for example to test a reward function on the same samples. Do not use it to lower the gap.

The cost

Deterministic kernels are slower. Four steps had no heat-guard pause in all four runs: steps 0, 1, 4, and 10. On those four steps, the median rollout took 56.5 s with the default engine. The 59.1 s of section 5.3 is the same default run over its 6 clean steps. It took 79.1 to 82.0 s in the three deterministic runs, with answers of about the same length.

That is 40 to 45 percent more rollout time, from one run of each setting on a GPU under heat pauses. Treat it as an unconfirmed result. Measure the cost on your own GPU before you leave the flag on for a long run.