Compressing Onboard Language Models for Space Robots under a Behavioral-Fidelity Requirement

Language models are being explored as the interface through which space robots interpret operator commands and select among verified skills.

Running them onboard calls for low-bit compression, and flight use adds requirements that generic post-training quantization leaves implicit: a fixed memory budget and storage format, a static artifact under configuration control, and behavior close to the full-precision model validated on the ground.

We treat compression as a ground-segment step and study QSelect, which composes one static low-bit model from compatible AWQ, GPTQ, and AutoRound checkpoints by assigning each linear module to one quantizer family and scoring every substitution at the final next-token distribution.

The default fidelity mode needs no labels or tuned thresholds and accepts a substitution only when the forward KL divergence from the BF16 reference decreases; an optional target-adapted mode adds answer likelihood on a small labeled set.

On DeepSeek-R1-Distill-Qwen-7B, Llama-3.1-8B, and Llama-3.2-3B at 3 and 4 bits, composed models reach lower mean held-out perplexity than every single-family model in all six settings, and the fidelity mode lowers perplexity and KL further.

In a matched scaffold, final-output scoring gives lower held-out KL than representation-similarity scoring in all 12 paired runs.

With labels from training or development splits of the evaluated task families, the target-adapted mode raises 3-bit pooled accuracy on the two Llama models by 4.3 and 4.5 points over the best single-family model.

At 3 bits, unquantized embedding and output layers make up 43-46% of the packed artifact.

All evaluations use general-domain benchmarks on a ground GPU; space-domain tasks and flight hardware are left to future work.