When Adding Arms Breaks Legs: Exploration Noise, Not Shared-Trunk Gradients, Governs the Collapse of From-Scratch Whole-Body RL for Humanoid Locomotion

We study whether a single reinforcement-learning (RL) policy controlling all joints of ahumanoid (whole-body RL) is a viable alternative to the prevailing modular design (apretrained lower-body locomotion policy with a separately controlled upper body) forcommanded velocity-height tracking on a Unitree G1. Working entirely on a single consumerGPU (RTX 4060 Ti, 16 GB) with Isaac Lab and the AGILE framework, we report a sharp,reproducible failure mode: extending the action space from the 12 leg joints to all 29 bodyjoints causes from-scratch training to collapse -- episodes terminate within ~2 steps andnever recover -- even when the arms are physically frozen. Through controlled ablations werule out code defects, action scaling, data-augmentation symmetry, observation contamination,and the harness curriculum. The natural hypothesis -- gradient interference in the shared MLPtrunk -- we test directly and reject: decomposing the per-update trunk gradient into leg-headand upper-head contributions shows them indistinguishable between the collapsing and learningregimes, and an actor with physically separate trunks collapses identically. A clean 2x2(trunk topology x noise) isolates the controlling variable to the policy's initialexploration noise sigma_0; reducing it restores stable learning in both architectures, with asharp, seed-dependent dose-response. A compute-matched pilot finds the modular design reachesa deployable policy under an equal budget while from-scratch whole-body RL does not (success1.0 vs. 0.25), reported cautiously with explicit confounds. Our contribution is areproducible, single-GPU diagnosis that directly measures and falsifies the most citedcandidate mechanism, attributing the failure to exploration scale instead, plus a releasedevaluation harness and a confound-controlled protocol. Code & data:- Harness, configs, logs, paper source: https://gitlab.com/KyulingLee/sim_rl_data_factory_nvidia- Diagnostic probes (gradient decomposition, separate-trunk actor, ratio/LR probes), WBC-AGILE fork branch agile-h1-phase1: https://gitlab.com/KyulingLee/wbc-agile-h1