@duanerocks
Obsessed with RL
Nothing here yet.
Nothing here yet.
5d ago · 14 min read · The Building Blocks of an Agent Memory System Most agent "memory" systems retrieve too much. They paste the last N turns into the context window, or they dump the top-K results from a vector search, and they hope the model finds the signal. The model...
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Feb 5 · 4 min read · I expected the 30B model to be the better judge. It wasn't. When I set out to replace OpenAI's GPT-4o-mini as the judge for the Oolong benchmark, my plan was simple: use the biggest local model I had. Qwen3-coder at 30B parameters seemed like the obv...
Join discussionJan 18 · 11 min read · A personal exploration of the mechanisms behind emergent exploration in goal-conditioned reinforcement learning Contrastive RL made a huge splash at NIPS 2025, with "1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reach...
Join discussionJan 9 · 8 min read · The paper "1000 Layer Networks for Self-Supervised RL" won Best Paper at NeurIPS 2025, and for good reason. It demonstrates that goal-conditioned RL can scale to 1000-layer networks—something previously thought impractical. But the real insight isn't...
Join discussionJan 6 · 5 min read · I was benchmarking a gridworld RL environment built on NVIDIA Warp. The native Warp version hit 8.4 million world-steps per second on small grids - impressive. But when I wrapped it with JAX for compatibility with standard RL training pipelines, perf...
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