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Found 7 papers, 3 papers with code
Debating with More Persuasive LLMs Leads to More Truthful Answers
In anticipation of this, we ask: can weaker models assess the correctness of stronger models?
Scaling Opponent Shaping to High Dimensional Games
In multi-agent settings with mixed incentives, methods developed for zero-sum games have been shown to lead to detrimental outcomes.
Leading the Pack: N-player Opponent Shaping
We evaluate on over 4 different environments, varying the number of players from 3 to 5, and demonstrate that model-based OS methods converge to equilibrium with better global welfare than naive learning.
JaxMARL: Multi-Agent RL Environments in JAX
This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL.
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning
Open-ended learning methods that automatically generate a curriculum of increasingly challenging tasks serve as a promising avenue toward generally capable reinforcement learning agents.
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
We present our findings as the starting point for further research into evaluating how LLMs interpret language in context and to drive the development of more pragmatic and useful models of human discourse.
Considering Race a Problem of Transfer Learning
We take the novel approach of considering race as a boundary for transfer learning in both the task (facial classification) and the domain (synthesis over distinct datasets).
