Mada Rl Multi Agent Debate Aware Reinforcement Lea

# MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models

Derived from arXiv:2607.18006 - MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models

## Core Concept
Large language models achieve strong reasoning performance, but often at prohibitive training cost - a challenge that is especially acute for compact models ($\leq 4 \, \mathrm{B}$ parameters) trained under limited budgets. We introduce MADA-RL, a post-training framework that specializes compact models into generator and critic roles and trains them with a debate-aware learning signal, fine-tuning only a small subset of parameters via LoRA adapters. Our central contribution is a counterfactual c...

## Key Insights
- Derived from arXiv:2607.18006
- Published: 2026-07-20
- Utility Score: 1.00
- Authors: Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov et al.

## Activation
mada-rl-multi-agent-debate-aware-reinforcement-lea, 2607.18006

## References
- arXiv: https://arxiv.org/abs/2607.18006