MORA rewrites prompts to expand multi-dimensional reward diversity and breaks the safety-helpfulness trade-off, improving sequential single-preference alignment by up to 12.4% and simultaneous alignment by 4.6%.
ExpLang improves LLM reasoning via on-policy multilingual thinking language selection during RL, outperforming English-only training and extending exploration with diverse language preferences.
FinMTM introduces a bilingual multi-turn multimodal benchmark with 11,133 financial visual QA pairs to evaluate vision-language models on reasoning and agent tasks, revealing significant limitations in fine-grained perception and complex workflows.