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#trust region

Exploration vs Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward

Intermediate

Peter Chen, Xiaopeng Li et al.Dec 18arXiv

The paper studies why two opposite-sounding tricks in RL for reasoning—adding random (spurious) rewards and reducing randomness (entropy)—can both seem to help large language models think better.

#RLVR#Group Relative Policy Optimization#ratio clipping

Understanding and Improving Hyperbolic Deep Reinforcement Learning

Intermediate

Timo Klein, Thomas Lang et al.Dec 16arXiv

Reinforcement learning agents often see the world in straight, flat space (Euclidean), but many decision problems look more like branching trees that fit curved, hyperbolic space better.

#hyperbolic reinforcement learning#Hyperboloid#Poincaré Ball

Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning

Intermediate

Zhenpeng Su, Leiyu Pan et al.Dec 5arXiv

Reinforcement learning (RL) can make big language models smarter, but off-policy training often pushes updates too far from the “safe zone,” causing unstable learning.

#reinforcement learning#PPO-clip#KL penalty

EtCon: Edit-then-Consolidate for Reliable Knowledge Editing

Intermediate

Ruilin Li, Yibin Wang et al.Dec 4arXiv

Large language models forget or misuse new facts if you only poke their weights once; EtCon fixes this with a two-step plan.

#knowledge editing#EtCon#TPSFT