Rlhf
Research papers, repositories, and articles about rlhf
Showing 5 of 5 items
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.
Reflective Preference Optimization (RPO): Enhancing On-Policy Alignment via Hint-Guided Reflection
Builds on Direct Preference Optimization but tackles its weak learning signal when both preferred and rejected responses share similar flaws. RPO adds a hint-guided reflection step that encourages the model to produce more contrastive, informative preference pairs before optimizing them. The result is a more stable and data-efficient on-policy alignment pipeline that still avoids full RLHF/RLAIF complexity.
Achieving Olympia-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning
InternGeometry is a geometry-solving LLM agent that reaches medalist-level performance on IMO geometry problems by tightly integrating with a symbolic engine. It proposes auxiliary constructions and propositions, verifies them symbolically, reflects on the feedback, and is trained with a complexity-boosting RL curriculum—achieving 44/50 problems solved using a tiny fraction of the data required by AlphaGeometry 2.