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 framework · source date 2026-06-01 · 0 comments ·
original1. Problems / challenges / motivations
- Every lab reimplementing PPO/GRPO plumbing wastes effort and makes results incomparable.
- Multi-turn agentic rollouts with tool calls do not fit training loops designed for single-turn completions.
- Rollout throughput becomes the bottleneck without async and load balancing.
2. Key ideas
- A production RL training...
product release · source date 2026-05-19 · 0 comments ·
original1. Problems / challenges / motivations
- IDE-centric tooling assumes a human in the editor, which is the wrong center of gravity once agents do most of the editing.
- Steering long-running agents needs surfaces for both synchronous and asynchronous work.
- Model and harness are usually developed by different teams and integrated late.
2. Key ideas
- A...
peer-reviewed paper · source date 2026-05-01 · 0 comments ·
original1. Problems / challenges / motivations
- Multi-agent debate and collaboration results were mostly established on weaker base models and assumed to hold as models improve.
- Teams have no decision rule for when to add agents versus when a single strong model suffices.
- Collaboration adds cost and latency that need justification.
2. Key ideas
- Quantifies...
research blog · source date 2026-04-01 · 0 comments ·
original1. Problems / challenges / motivations
- Multi-agent systems are adopted on the assumption that more agents means better results, with little controlled evidence.
- Comparisons across papers confound topology, model, and token budget.
- Errors propagate differently depending on how agents are wired together, and that effect is rarely measured.
2. Key...
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...