During training, two copies of the same small model alternately extend a shared reasoning trace.
Small models can steer stronger ones through shared reasoning traces
Allspark trains a weak model to contribute reasoning segments that improve larger, fixed models without using their outputs during training.
Big Tech
Kaizhao Liang · Junxiong Wang · Chen Liang · Zhendong Wang · Qiang Liu
UT Austin · Together AI · Microsoft
Research Digest··3 min read
Liang and colleagues introduce Allspark, a framework for transferring reasoning behavior from a reinforcement-learned small model to stronger models through alternating text-based chains of thought.
Why this paper
From Microsoft and 2 others
In one line
Weak teacher trained with RL improves strong student accuracy by alternating reasoning chains without strong model rollouts.
What we could check
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