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Hindsight Experience Replay
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arXiv AI
3d ago
ETHER: Aligning Emergent Communication for Hindsight Experience Replay
arXiv:2307.15494v3 Announce Type: replace-cross Abstract: Hindsight Experience Replay (HER) enhances sample efficiency in goal-conditioned reinforcement learning (RL) by relabelling failed trajectori...
arXiv Machine Learning
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HindSearch: Trajectory-Level Hindsight Critique for Search-Augmented Reinforcement Learning
arXiv:2608. 01597v1 Announce Type: new Abstract: Search-augmented LM agents are typically trained with a binary exact-match reward, which throws away most of what a failed trajectory tells us about why it failed.
OpenAI Blog
Feb 26, 2018
Ingredients for robotics research
We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for our research over the past year. We’ve used these environments to train models which work on physical robots.
arXiv AI
Sep 4
Headroom-Drift Replay: A Primitive for Principled Replay Control in GRPO
Headroom-Drift Replay is a replay control primitive designed for GRPO that separates reuse into two decisions: Headroom ranks stored groups by remaining learning value, while Drift gates them by compatibility with the current policy. The method keeps the fresh on‑policy stream unchanged and adds no auxiliary generation or training machinery. Across mathematical reasoning, multimodal reasoning, and Agentic Search benchmarks, it outperforms naive replay and matches or exceeds broader replay methods on Avg Mean@32, delivering comparable quality at materially lower wall‑clock time in Agentic Search.
arXiv Machine Learning
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Trajectory-Relative Hindsight Distillation for Agentic Reinforcement Learning
arXiv:2608. 07371v1 Announce Type: new Abstract: Recent agentic reinforcement learning methods use hindsight to complement sparse outcome rewards.
arXiv Computation and Language
Sep 1
Hindsight Memory-PRM: Supervising Memory Management with Auditable Hindsight Credit
arXiv:2608.29605v1 Announce Type: new Abstract: Memory operations of long-horizon LLM agents are hard to supervise: an operation's value is unobservable when it is taken. But they are special -- they...