REINFORCEMENT-LEARNING

30 days · UTC

LIVE_DATA_STREAM // APRIL_14_2026

Synchronizing with global intelligence nodes...

DENSITY_RATIO: MAX
AI-AGENTS
MAR_25 // 07:36

Karpathy’s agentic workflow: from coding to manifesting intent

Andrej Karpathy says his workflow flipped to delegating most coding to AI agents since December 2024. In a wide-ranging recap, Karpathy describes a s...

NVIDIA
MAR_14 // 07:50

Decouple RL environments from training: NeMo Gym + Unsloth approach, backed by new failure-mode evidence

A new deep dive argues RL teams should separate environment services from the training loop, and fresh research shows why sloppy environments create b...

SAMPLE-POLICY-OPTIMIZATION
MAR_06 // 10:30

Stabilizing Agentic RL and Closing Multilingual Alignment Gaps

New research points to a more stable RL path for long-horizon LLM agents and exposes multilingual alignment gaps that can surface unsafe or inconsiste...

JET-RL
JAN_23 // 16:44

Jet-RL claims 41% faster RL training via FP8 'Unified Precision Flow'

Jet-RL reports a 41% training speedup in reinforcement learning by using FP8 with a "Unified Precision Flow" that coordinates precision choices across...

JET-RL
JAN_23 // 16:11

Jet-RL claims 41% faster RL via FP8 unified precision

A report on [Jet-RL](https://quantumzeitgeist.com/41-percent-rl-faster-reinforcement-learning-jet-achieves-fp8-unified-precision/)[^1] says its unifie...

JET-RL
JAN_23 // 15:39

Jet-RL claims 41% faster RL training via FP8 unified precision

Jet-RL reports a 41% speedup in reinforcement learning by using FP8 with a "unified precision flow," suggesting a consistent precision strategy across...

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