Qwen3 Multi-Domain RL Integration: MOPD is the Most Balanced
burny_tech · x · 2026-07-18
This discusses the integration of multi-domain capabilities in Qwen3-30B-A3B, focusing on the MOPD method and comparing it against various baselines.
Results Overview
- MOPD achieves the highest normalized total score of 0.9373 among six integration methods.
- The runner-up is Mix-RL, with a normalized score of 0.8818.
- MOPD shows much more balanced performance across domains, with normalized scores in three domains falling between 0.91–0.95, exhibiting the smallest variance.
Comparative Conclusions
- Cascade RL: Suffers from cross-domain interference, where capabilities learned in earlier stages are degraded in subsequent stages.
- Off-Policy Finetune: Surpasses the teacher on IF (instruction following) but only closes SWE to about 65%.
- Mix-RL: Serves as a more balanced baseline but still lags behind MOPD overall.
Key Takeaway
MOPD's advantage lies not just in a higher total score, but in its uniform improvements across tasks, proving it is better suited for multi-domain capability integration rather than just optimizing for a single metric.
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