happynood/quant-control-bench-policies

Go1 locomotion policies at nine precisions

Try them in your browser · Code and methodology

One PPO policy for Go1JoystickFlatTerrain, exported to ONNX nine times at different weight precisions, plus the scene it was trained in. The point of the set is the comparison: how far precision can be reduced before a closed-loop controller stops working, and where that boundary actually sits.

Full methodology, code and raw results: https://github.com/Happynood/quant-control-bench

Results

100 episodes x 5 fixed seeds, 1000-step horizon, deterministic policy (mean action, no sampling). Return deltas are paired bootstrap 95% intervals over 10 000 resamples. P50 is the perturbation magnitude at which the success rate crosses 50%; on friction_scale, swept downward, a larger number is worse.

scheme	bits/weight	mean return	Δreturn vs fp32 (95% CI)	P50 friction	P50 obs noise
fp32	32.00	31.65	—	0.189	0.142
fp16	16.00	31.65	no measurable loss	0.192	0.143
int8-tensor	8.00	31.68	no measurable loss	0.200	0.142
int8-channel	8.00	31.66	no measurable loss	0.205	0.142
int4-channel	4.00	30.91	-2.336% [-2.79%, -1.95%]	0.241	0.127
int4-group32	4.00	31.42	-0.728% [-0.88%, -0.45%]	0.207	0.140
ternary	1.58	2.95	-90.672% [-92.14%, -89.13%]	1.000	0.000
mixed-head-fp16	4.19	31.36	-0.902% [-1.26%, -0.70%]	0.198	0.136
int8-act	8.00	31.65	no measurable loss	0.193	0.142

What the numbers say

• int8 is free. Every int8 variant, including one that quantizes activations as well as weights, is statistically indistinguishable from fp32 on return and on all five robustness axes.
• The boundary is between int8 and int4, and grouping matters more than bits. int4-channel loses only 2.3% of return on flat ground yet its friction and observation-noise frontiers separate from fp32 with non-overlapping intervals. int4-group32, at the same 4.00 bits, does not.
• Quantizing the observation-normalization statistics is catastrophic. They are 0.05% of the parameters. Quantized with the same scheme as the weights, every int8 variant becomes measurably lossy and every 4-bit variant stops producing finite actions at all, because a strictly positive scale vector quantized symmetrically rounds entries to zero and the policy divides by them.

Intended use and limitations

These are research artifacts for studying quantization of closed-loop control. They are not tuned for deployment on hardware and have never been run on a physical robot.

• Quantization is simulated. Weights are rounded to the target grid and stored back as float32, so every ONNX file is the same size and none of them runs faster. The benchmark measures what precision does to control, not what it does to storage or throughput. A deployment would need packed kernels, and the accuracy results here would carry over while the timing results would not.
• One training seed. Precision effects cannot be fully separated from the luck of a single checkpoint.
• One task, flat terrain. Go1JoystickFlatTerrain only. The headline hypothesis (that open-loop action error mispredicts closed-loop performance) is not supported on this task, and that negative result is reported with the same prominence as the positive ones.
• Browser physics differs from training physics. The demo runs MuJoCo 3.3.8 compiled to WebAssembly; training used MJX with MuJoCo 3.10.0. Measured divergence and the reasoning behind it are in the repository's methodology.

Training

Environment steps executed	206,438,400
Wall clock	140.0 min
Parallel environments	8,192
Peak VRAM	2225 MiB
Final training reward	31.772 ± 1.810
Weight extraction vs Brax	4.47e-06
Hardware	NVIDIA RTX 3050 Laptop, 4096 MiB

Every ONNX graph carries the observation normalization inside it, so the input is the raw 48-dim observation and the output is the tanh-squashed action.