Agent fixes looping Qwen3.5 model by editing weights directly, no training needed
VictorSanh · x · 2026-11-12
After a human fixed a looping model by editing its weights, the Beaker team gave the same task to their autonomous agent-improver Beaker — which never ran the model yet autonomously found a different fix. Dubbed "weight-space programming," the approach involves no training or gradients. Target: Qwen3.5 0.8B, which fails 93% of MATH-500 by getting stuck in loops until thinking tokens run out. Beaker analyzes failure behavior, hypothesizes fixes, and hill-climbs a score, ultimately turning down the model's tendency to generate "Wait."
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