Liquid AI Details Post-Training Pipeline: 4 Stages and Multi-Turn Agentic RL
Teknium · x · 2026-08-04
Liquid AI shared an in-depth look at the post-training pipeline of their on-device models, emphasizing that models are trained directly inside real-world agent harnesses.
Technical Implementation:
- Four Stages: The process includes SFT, expert specialization, multi-domain on-policy distillation, and agentic RL.
- Multi-Turn Agentic RL: The final stage utilizes multi-turn reinforcement learning via Pi, Hermes Agent, and OpenClaw.
- Sandbox & Rewards: Each rollout runs in its own sandbox, optimized with GRPO against an outcome reward based on an LLM-as-a-judge rubric, programmatic checks, and a hard safety gate. This ensures the model is already familiar with specific tools, system prompts, and interaction patterns upon release.
More from Research
- Profluent's New CRISPR Approach Expands Targetable Mutations by 10X — nathanbenaich · 2026-08-04
- Essay argues the 'Stochastic Parrot' concept is wrong and harms AI ethics — _FelixSimon_ · 2026-08-04
- MIT Proposes Reusable Failure Analysis Framework for Multimodal Clinical AI — MIT · 2026-08-04
- Eric Horvitz Proposes Decision-Analytic Steering for High-Stakes LM Decisions — erichorvitz · 2026-08-04
- Nature Medicine Study: LLMs Excel at Triage Discrimination But Lack Calibration — erichorvitz · 2026-08-04
- OpenBMB Launches Dual-Agent System to Automate Supercomputing Acceleration — aigclink · 2026-08-04