Verifiability Under Uncertainty: Robust RLVE for Noisy, Partially Observable, or Open-World Language Tasks

Research Question: Can RLVE-trained LMs achieve robust reasoning and adaptability when environments include uncertainty, partial observability, or probabilistic verification—more faithfully reflecting real-world conditions?

Hypothesis: Incorporating uncertainty and noise into RLVE environments will better prepare LMs for real-world tasks, increasing their ability to generalize and recover from ambiguous or corrupted signals.

Experiment Plan: - Design new RLVE-Gym environments where the reward signal is occasionally noisy, delayed, or only partially reveals the ground truth (e.g., masked input/output, probabilistic verification).

• Train LMs on a mix of standard and noisy environments.
• Evaluate on both noiseless and noisy versions of reasoning benchmarks, measuring robustness, recovery, and calibration.
• Analyze the tradeoff between learning stability and real-world adaptability.

References: ['Zeng, Z. et al. (2025). RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments.', 'Cabrejas Egea, A., Howell, S., Knutins, M., & Connaughton, C. (2020). Assessment of Reward Functions for Reinforcement Learning Traffic Signal Control under Real-World Limitations. IEEE International Conference on Systems, Man and Cybernetics.']