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DelTA identifies a structural problem in RLVR training: the gradient signal used to improve reasoning models is dominated by high-frequency formatting tokens rather than the tokens that actually distinguish good responses from bad ones. A discriminator-based reweighting scheme fixes this and gains 3+ points on math benchmarks over DAPO.
Read more →A new paper from UIUC shows that continuous memory consolidation — the pattern of having an LLM rewrite its own experiences into stored lessons — can degrade agent performance below the no-memory baseline, sometimes dramatically. GPT-5.4 fails 54% of ARC-AGI problems it had previously solved with clean trajectories after those solutions pass through a consolidation loop. An episodic-only agent that retains raw rollouts without abstraction beats every consolidator tested across five benchmarks.
Read more →A preprint from the DeepReinforce Team claims their GrandCode system placed first in three consecutive live Codeforces rounds in March 2026, defeating all human participants. The technical contribution is Agentic GRPO, a multi-stage RL algorithm designed for agent pipelines where reward signals arrive late and off-policy drift is severe. Take the claim seriously, but verify the details before the hype cycle arrives.
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