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News/Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning
arxiv
PublishedJuly 18, 2026 at 4:00 AM
▲bullish

Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.14171v1 Announce Type: new Abstract: Reinforcement learning has emerged as the dominant paradigm for training large language model (LLM) agents that interact with executable sandboxes. State-of-the-art algorithms such as PPO, RLOO, and GRPO inherit their rollout topology from RLHF: for ea

Models mentioned
01
  • 01meta-llama logo
    Llama-3.1-70B
    meta-llama/Llama-3.1-70B
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Discussion
Mentioned models
05
  • 01
    PPO
  • 02
    RLOO
  • 03
    GRPO
  • 04
    Qwen2.5-7B
  • 05
    Llama-3.1-70B
    meta-llama/Llama-3.1-70B
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#reinforcement learning#language models#optimization

No replies yet. Be first.

Mentioned models
05
  • 01
    PPO
  • 02
    RLOO
  • 03
    GRPO
  • 04
    Qwen2.5-7B
  • 05
    Llama-3.1-70B
    meta-llama/Llama-3.1-70B
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#reinforcement learning#language models#optimization
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Originally published on arxiv ↗
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