Meta-Learning for Task-Adaptive Multi-Answer Generation

TL;DR: Imagine a model that learns how to generate the right kind of answer set depending on the task—be it coding, medical, or ambiguous trivia. We’ll meta-train models to rapidly adapt their multi-answer strategies to new domains.

Research Question: How can meta-learning be leveraged to enable multi-answer RL models to generalize their distributional reasoning strategies across a wide variety of tasks and domains, including those unseen during training?

Hypothesis: Meta-trained multi-answer RL models will quickly adapt to new distributional reasoning tasks, outperforming single-task or naive transfer approaches in both answer diversity and calibration, especially with limited adaptation data.

Experiment Plan: - Adapt the meta-learning approaches from Dang et al. (2025) and Bose et al. (2025) to the multi-answer RL setting: train across a suite of tasks (e.g., QA, coding, clinical reasoning) with the objective of quick adaptation.

• Structure each “episode” as a new task/domain with its own answer distribution; reward adaptation speed and final calibration/diversity.
• Test on held-out domains (e.g., rare medical cases, new coding languages) and measure adaptation efficiency and set-level metrics.
• Ablate the impact of meta-learning vs. standard multi-task training.

References:

• Dang, Y., Xu, J., Yang, F., Jiang, C., & Li, D. (2025). Meta Reinforcement Learning Based Adaptive and Interpretable Energy Storage Control Meets Dynamic Scenarios. IEEE Transactions on Sustainable Energy.
• Bose, S., Singha, M., Jha, A., Mukhopadhyay, S., & Banerjee, B. (2025). Meta-Learning to Teach Semantic Prompts for Open Domain Generalization in Vision-Language Models. Trans. Mach. Learn. Res.