Series
30 days, 30 papers.
One paper a day, explained simply.
Day 1
Stop agents from gaming the test
RRSI: Regularized Recursive Self-Improvement of Agent Harnesses
Day 2
Teach models to win clean
MiMo-V2.6: Scaling Up Reinforcement Learning for Self-Improvement
Day 3
A week old, already tested
JEV-as-a-Judge: Accept When Confident, Escalate When Unsure
Day 4
The rich get richer
Never Give Up: Learning to Solve Hard Problems in RL for LLMs
Day 5
Grade the process, not the output
OSWorld-Pro: evaluating where computer-use agents fail
Day 6
Train the search, keep the model frozen
Beyond Repeated Sampling: concepts guide the search