The authors studied heterogeneous teams, in which different language models attempt the same problem and exchange reasoning over multiple rounds.
Progressive pruning lets language-model debates beat equal-cost baselines
Conditional Progressive Pruning uses intermediate debate results and learned agent behavior to allocate computation across rounds more effectively.
Top University
Ruosong Ye · Caiqi Zhang · Jiahao Li · Haijun Wu · Xiaolong Luo · Huiyuan Chen · +6 more
Rutgers University, New Brunswick · University of Cambridge · Tsinghua University · Harvard University · Case Western Reserve University
Research Digest··3 min read
Ye and colleagues test whether multi-agent debate offers benefits beyond independent sampling when methods receive the same computational budget.
Why this paper
From University of Cambridge and 6 others
In one line
Conditional Progressive Pruning makes multi-agent debate outperform consistency methods under the same computational cost.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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