arXiv (All)
open access
GTRL: Grounding Divide-and-Conquer Value Learning with Temporal Differences
In offline goal-conditioned reinforcement learning (GCRL), divide-and-conquer scales to long horizons by joining two shorter segments at a subgoal. However, under stochastic dynamics, the base case of this rule values the luckiest trajectories through the data. The subgoal must also lie on a shared
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
- TIGER-Lab/MathInstruct#786662
- Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost#819658
- Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost#809662
- PEER: Unified Process-Outcome Reinforcement Learning for Structured Empathetic Reasoning#617852
- PEER: Unified Process-Outcome Reinforcement Learning for Structured Empathetic Reasoning#617352
Record · ID 1117971
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