MOA: Multi-Objective Alignment for Role-Playing Agents
Summary
MOA gets called out as a way to align role-playing agents along many competing dimensions simultaneously using multi-objective RL and thought-augmented rollouts. It’s especially relevant if you’re trying to get smaller models to behave like premium chatbots in complex, persona-heavy domains. ([huggingface.co](https://huggingface.co/papers/2512.09756))
Related Content
Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation
This paper is a systematic exploration of reinforcement learning for text-to-3D generation, dissecting reward design, RL algorithms, data scaling, and hierarchical optimization. The authors introduce a new benchmark (MME-3DR), propose Hi-GRPO for global-to-local 3D refinement, and build AR3D-R1—the first RL-tuned text-to-3D model that improves both global shape quality and fine-grained texture alignment.
OPV: Outcome-based Process Verifier for Efficient Long Chain-of-Thought Verification
OPV (Outcome-based Process Verifier) is a verifier model that inspects the rationale steps of long chains-of-thought via summarized outcomes, combining the strengths of outcome-based and process-based verification. Trained with an active learning loop, rejection fine-tuning, and RLVR, OPV reaches strong F1 on OPV-Bench and outperforms much larger models like Qwen3-Max-Preview at detecting reasoning errors.
Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving
This work presents a long-horizon reasoning agent for Olympiad-level math that uses an Outcome-based Process Verifier (OPV) to supervise and clean up very long chains-of-thought. By summarizing and checking reasoning segments rather than only final answers, and training OPV via iterative active learning and RLVR, the system achieves new SOTA on a held-out benchmark while reducing annotation cost.
T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground
T-pro 2.0 is an open-weight Russian large language model focused on hybrid reasoning: it can answer directly or emit explicit reasoning traces, and it’s optimized for low-latency inference via speculative decoding. Alongside the model, the authors release a Russian instruction corpus, a math benchmark, and an EAGLE-based inference stack, making it a practical foundation for Russian-language reasoning applications.