arXiv paper · source date 2026-06-24 · 0 comments ·
original1. Problems / challenges / motivations
- Self-improving agents are bounded by the evaluator that scores their modifications, so a fixed evaluator caps improvement.
- In hard-to-verify domains — paper writing, proof grading — there is no benchmark to serve as the evaluator at all.
- Letting the evaluator evolve freely invites the agent and evaluator to drift...
peer-reviewed paper · source date 2026-06-01 · 0 comments ·
original1. Problems / challenges / motivations
- Best-of-N spends the same verification compute on easy and hard candidates alike.
- Generative verifiers are accurate but expensive, which limits how often they can be called.
- Verifier training and verifier-guided search are usually studied separately.
2. Key ideas
- FlexiVe is a generative verifier that...
open-source tool · source date 2026-04-01 · 0 comments ·
original1. Problems / challenges / motivations
- RL on agent tasks needs a reward function, and most real tasks have no verifier and no labeled data.
- Hand-crafting reward functions per task is the main cost of applying RL to agents.
- Absolute LLM-judge scores are noisy and poorly calibrated across tasks.
2. Key ideas
- A general-purpose reward function that...
technical report · source date 2026-02-01 · 0 comments ·
original1. Problems / challenges / motivations
- RLVR works where rewards are verifiable, which excludes most multimodal and open-ended agentic tasks.
- Scaling agentic post-training needs a reward signal that does not depend on hand-written verifiers for every domain.
- Single-agent rollouts limit the diversity of trajectories available to learn from.
2. Key...