Compile real-world Claude Code and Codex trajectories into verified, tradable post-training assets.
Procedural data generators for verifiable reasoning, synthetic pretraining, post-training, evaluation, and RL.
Context & Guide For Reinforcement Learning with Verifiable Rewards with Large Language Models
Score the trustworthiness of outputs from any LLM in real-time
🐀 Fuzz your verifier before an RL agent does. Static + dynamic LLM security auditor to detect reward-hacking in RL post-training environments (OpenEnv, verifiers-spec, Gymnasium).
A curated list of rubrics, checklists, criteria sets, principles, and scoring guides used to score, rank, verify, filter, or train modern generative models.
Write loops, not prompts. The loop is easy — the verifier is the whole game. VCN night one at Network School.
Open Arena: SLM verifiers across observability platforms, dataset and model catalogs, and value scenarios for LLM, agentic and harness evals.
An RL Enviorment for AES Inversion
A verifiers RLM environment for testing whether adaptive recursive search outperforms brittle manual RAG choreography on long synthetic corpora.
Adversarial QA for LLM-RL environments: find out what reward an empty answer earns. Model-free, zero API cost.
Verifiable RL environments for corporate law & governance — deterministic reward, no LLM judge, fully synthetic worlds.
An open reinforcement-learning (RL) environment that trains LLM agents to use the current fact, not the stale one — verifiable reward for temporal fact-currency, built on verifiers / prime-rl (GRPO, LoRA).
Reproducible verifier audits, datasheets, agreement metrics, and release gates
Typed asset shapes + visual + headless views for AI agents. One asset definition. Three rendering targets (HTML / Markdown / Text).
RLVR coding environment for training and evaluating LLM agents on automated code-fixing tasks.
Research proposal for verifier-gated on-policy distillation with explicit evaluation and claim boundaries
A verifiable RL environment for TRP ion-channel ligand pharmacology, built on Prime Intellect's Verifiers
Deterministic SRT captioning environment for LLM evaluation and reinforcement learning.
Running, reproducing, and testing AI agent environments to understand how they work.
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