Applying LLM Sampling Params to Robots: Low Temperature Causes Hilarious Fails
carlosdponx · x · 2026-08-03
A developer experimented with applying LLM sampling parameters like temperature and top-p to a robot action generation model (GPC). While default temperature 1.0 works fine, lowering it to 0.1 heavily skews the probability of likely actions. This causes the robot's movements to distort hilariously, resulting in an accidental split and a complete loss of momentum.
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