r/reinforcementlearning
Reinforcement learning research discussion.
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Latest from r/reinforcementlearning
- Breaking down how Anthropic actually achieved a 2x R&D speedup in just one yearhá 1 d
- [P] Pilot: self-play checkpoints that judge bot fights better judge human fights worse (ρ = −0.73), and my opponent-model "memory gain" turned out to be a baseline artifacthá 2 d
- Blogs kind of resources for RLhá 2 d
- PPO on Temple Run: pixels did worse than random, so I read the game's memory instead (lessons + 30-run eval)há 2 d
- Multiple robots in one MuJoCo world vs. one robot per parallel environment.há 2 d
- [Project] Aionterra: learning from different bodies in one continuous worldhá 3 d
- In long-horizon agentic RL, tokens/sec is the wrong metric. Your trainer is waiting on stragglershá 4 d
- "Could a modern chess engine on a Pentium have defeated Kasparov in 1996?", Nicholas Carlini 2026-11 (another chess time travel experiment)há 4 d
- Thoughts on current state of RLlib?há 4 d
- Rebuilt the "inside the RL agent's brain" visualization on corrected results two of its old claims didn't survivehá 4 d
- Corrected version of my "can an RL agent tell its world is fake" experiment: a GAE bootstrap bug barely moved the result, but follow-up controls changed what it meanshá 4 d
- SARSwarm: 4 AI drones (Multi-head) SAR mission (Metis-Core v0.4.2-Alpha)há 5 d
- I trained a 4-drone autonomous combat swarm using continuous PPO in Unity 6 with sub-0.1ms Sentis inference (Zero NavMesh)há 5 d
- [Research] Does LeWorldModel preserve local action geometry? Implications for model-based RL and planninghá 5 d
- Tried Claude + ElevenLabs to turn my article into a video - here's the summary at the end, full video link in the descriptionhá 5 d
- Fine-tuning a humanoid locomotion policy on scanned real-world sand: stock policy fell, fine-tuned held. How do you validate terrain physics in sim?há 5 d
- Help me build a superhuman STS2 teacher (OstyAI)há 6 d
- DeepSeek's "Exploring Super-Agent Design" explained in under 3 minutes! A fresh perspective on how agentic AI harnesses are designed and why they matter.há 6 d
- Frame sampling can't prove something isn't in a videohá 6 d
- whichever the current rules list Same text, shortened, with focus on imitation learning data requirements.há 6 d
- critique wanted: my agents' learning rate and discount come from their personality, plus a reward penalty i know is wronghá 6 d
- Exponential tilt view of RLVR: Stiemke duality decides when a pass@1 gain is free, and Karlin total positivity bounds the pass@k crossingshá 6 d
- League of Legends (Patch 26.19) - Ezreal AI 1v1há 6 d
- Reward misspecification in documented cases (CoastRunners and others): how do you check that a high-return policy does what you intended?há 6 d
- RL From Scratch: Introducing my NeatRL Library for Running RL algorithmshá 7 d