Antidote and Hemingway-bench leaderboards C
AI benchmark · Enterprise Workflows
- Publisher
- Surge AI
- Domains
- Enterprise Workflows Long-Horizon
- On X
- @HelloSurgeAI ↗
- Tier
- C why
- Snapshot
Full listing name: Antidote and Hemingway-bench leaderboards - https://surgehq.ai/benchmarks
Latest from Surge AI on X
@HelloSurgeAI ↗What is Antidote and Hemingway-bench leaderboards?
Surge AI's benchmark leaderboard ranks AI models by their capacity for rigorous reasoning and real-world mastery.
General capability mostly
“This is our ranking of models, measured by their capacity for rigorous reasoning and real-world mastery.”
surgehq.ai/benchmarks ↗
Tier C
Niche: a handful of models scored, or built by an RL vendor.
How much stands behind a tier: usage, who backs it, a confirmed line from its own site or paper, and a clear read on X. High with three of the four, medium with two, low otherwise.
- built by Surge AI
- in curated RL directories (2 pts)
How we got to tier C
- Adoption: built by Surge AI. Tier C.
- Not yet read on X.
- In curated RL directories: 2 points, a floor of C.
- Confirmed: a line from its own site or paper places it.
A clear view on X moves a tier one step up or down; most entries don't move. How tiers work.
Training methods
Methods Antidote and Hemingway-bench leaderboards uses, offers or builds for, with public evidence: its own website says so, or two independent sites do.
- RLHF Reinforcement Learning from Human Feedback On its own website
“Overview: Anthropic is One of the World’s Leading AI and LLM Companies The Problem: Building Trustworthy, High-Quality Human Feedback The Solution: Rich Human Feedback via Surge AI’s RLHF Platform Results: Anthropic’s Claude – a Safe, Highly Capable, State-of-the-Art AI Assistant”
surgehq.ai“Children (and adults and AI) learn from preferences and rewards: praise a ballet dancer when her movements flow, and she’ll become better and better. The RLHF data we generate embodies the richnesses and subtleties of the world — the notes that separate the ordinary from the inspired, Salieri from Mozart.”
surgehq.ai - RLVR Reinforcement Learning with Verifiable Rewards On its own website
“To check whether ComplexConstraints measures something worth optimizing for, we built a companion training set of 1,000 examples, and trained Qwen3-4B using RLVR with a judge model.”
surgehq.ai“ComplexConstraints and Beyond: Expert Rubrics for RLVR”
surgehq.ai - GRPO Group Relative Policy Optimization On its own website
“…fic work that real jobs demand. Frontier models such as GPT-5.2 and Claude Opus 4.6 solve fewer than 30% of tasks when all expert-authored rubric criteria must be satisfied. Using this environment, we train GLM 4.6 with Group Relative Policy Optimization (GRPO) and adaptive clipping. After a single epoch of training, the model improves from 25.37% to 36.76% task pass rate on held-out evaluation tasks. More importantly, these gains transfer to out-of-distribution benchma…”
surgehq.ai“- CoreCraft (enterprise RL Environment): GRPO with rubric-based rewards moved GLM 4.6 by +11.4pp on held-out tasks and transferred out-of-distribution: +4.5pp on BFCL Parallel, +7.4pp on τ²-Bench Retail, +6.8pp on Toolathlon — while Pass³, a reliability metric requ…”
surgehq.ai
Where Antidote and Hemingway-bench leaderboards sits
Its realm in the universe, live. Hover a star for its name; click to open it. Open it in the universe
Next: browse all benchmarks. Entry from the RL Research daily scrape of public sources, 2026-10-11 snapshot.