arXiv (All)
open access
TSBPO: Tree-Structured Branching for Policy Optimization in Interactive Language Agents
Training interactive language agents from terminal outcomes poses three coupled problems: which task and state continuations to sample, how to assign delayed feedback to decisions, and how to aggregate token losses for multi-token actions. We present TSBPO, a tree-structured sampling-to-update parad
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
- TSBPO: Tree-Structured Branching for Policy Optimization in Interactive Language Agents#1514978
- EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models#630018
- EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models#628156
- NLPG: Natural-Language Policy Gradients for Self-Evolving Language Agents#1118091
- T2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning#1155323
Record · ID 1535021
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