arXiv (All)
open access
Unified Trajectory Matching Policy Optimization: Diverse T2I Generation and VLA Generalization
Reward-maximizing reinforcement learning (RL) is widely used to post-train stochastic diffusion and flow policies for text-to-image (T2I) generation. However, reward-maximizing RL causes policy mode collapse even under reference KL or entropy regularization, reducing the policy to a single high-rewa
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- DCRL: Decoupling and Coupling Reinforcement Learning via Policy-Reward Manifold Alignment#1064116
- Evaluating Deep Actor–Critic Methods for Path Planning of Mobile Manipulators Under Wheel–Terrain Interaction#1092398
- Official Review · ICLR 2024#787004
- Demystifying Reinforcement Learning Post-Training of Language Models#792830
- Demystifying Reinforcement Learning Post-Training of Language Models#788826
Record · ID 1128947
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive