The Verifier Design Playbook: How to Build RLVR Gyms That Models Can't Game
In traditional machine learning, your loss function is a clean mathematical equation: mean squared error, cross-entropy, or cosine distance. The mat…
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Multi-Reward Reinforcement Learning for LLM Agents: Comparing PPO, GRPO, DAPO, and GDPO
New empirical follow-up: Part 2 compares seven trainer configurations on Qwen3-14B and our DEX gym , with no-think and thinking holdouts, interactiv…
RL 3: Bellman Equations and Markov Decision Processes (1950s–1960s)
Where we left off Two quick reminders. Thorndike (1898) watched cats escape a puzzle box and gave us the Law of Effect : do something, get a good re…
Building and Testing Robot Policies with MuJoCo
Building and Testing Robot Policies with MuJoCo MuJoCo (Multi-Joint dynamics with Contact) is a fast, accurate physics engine that has become a stan…
Can you steal a robot's next move by watching its clock? Journal of our experiments on timing side channels in multi agent RL
I have been keenly interested in the intersection of multi-agent reinforcement learning (MARL) and hardware security. When you deploy a trained RL p…
Decoding the Link Between Pretraining and Reinforcement Learning
What Happened Researchers have published a study investigating the complex pipeline from pretraining to reinforcement learning (RL) in large languag…
The Whole Paper Fits in One Sigmoid: Implementing the SDAR Gate
Recap. Part 1 framed the problem (trajectory reward is too coarse for multi-step agents) and SDAR's fix (a privileged teacher gives dense token-leve…
How to Add Live Telemetry and Failure Diagnosis to Isaac Lab, MuJoCo, or Gazebo Training in Under 5 Minutes
If you train robot policies long enough, you eventually realize the main problem is not launching runs. It is answering these questions fast enough:…