5 appearances on the backlist front page in the last 30 days.
1/4: A couple notes on the implementation. The async RL training itself is powered by SkyRL, with the research agent’s goal being resolving setup issues (in this case a libnuma dependency) and analyzing runs autonomously.
alphaXiv is deploying agents to set up arXiv codebases, resolve environment issues, reproduce core claims, and rank papers by implementation difficulty
“Trust Region On-Policy Distillation” On-policy distillation is powerful, but one bad mismatch between student and teacher can negatively impact the gradients. So this paper's TrOPD only learns where the teacher is reliable, treats outlie
A sample-complexity theory argues that hidden hierarchical data makes token prediction harder with depth while latent prediction avoids that blowup
MiniMax-M2 paper just dropped The key focus of M2 is on something more agent-native. It trains on runnable workspaces and artifact-grounded rewards, then uses Forge to scale RL over long coding, app, search, and office-task trajectories.