@Vtrivedy10: there's a very exciting future agent recipe for building intelligence too cheap to meter, applied towards extracting si…

X AI KOLs Following Papers
fine-tuning open-models llm-as-judge continual-learning data-mining langchain firework-ai

Summary

The post outlines a future agent recipe for building scalable intelligence by fine-tuning efficient, specialized open models to surpass frontier performance on LLM-as-a-judge tasks, and applying this to extract signals from trace data for continual learning. LangChain Labs and FireworksAI release new work demonstrating this approach.

there's a very exciting future agent recipe for building intelligence too cheap to meter, applied towards extracting signals from every single Trace agents produce it involves: 1. Fine-tuning efficient, specialized open models that reach frontier performance on narrow, important tasks 2. Understanding Trace data at massive scale so we can extract signals to improve every agent over long-time horizons --> Continual Learning framed as a Data Mining problem we're excited to release some new work from LangChain Labs with the awesome folks @FireworksAI_HQ (shoutout @chahvivi and the excellent team over there) we find that with good data design + SFT, builders can surpass frontier performance on LLM-as-a-judge tasks that read every Trace agents produce & extract signal from them via rubrics reach out if any of this is interesting - and if you want to fine-tune your own judges to process every trace at scale
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Cached at: 06/16/26, 07:39 PM

there’s a very exciting future agent recipe for building intelligence too cheap to meter, applied towards extracting signals from every single Trace agents produce

it involves:

  1. Fine-tuning efficient, specialized open models that reach frontier performance on narrow, important tasks

  2. Understanding Trace data at massive scale so we can extract signals to improve every agent over long-time horizons –> Continual Learning framed as a Data Mining problem

we’re excited to release some new work from LangChain Labs with the awesome folks @FireworksAI_HQ (shoutout @chahvivi and the excellent team over there)

we find that with good data design + SFT, builders can surpass frontier performance on LLM-as-a-judge tasks that read every Trace agents produce & extract signal from them via rubrics

reach out if any of this is interesting - and if you want to fine-tune your own judges to process every trace at scale

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@Vtrivedy10: https://x.com/Vtrivedy10/status/2066571435871551655

X AI KOLs Timeline

A joint study by LangChain Labs and Fireworks AI demonstrates fine-tuning an open Qwen model to create a trace judge that detects 'perceived error' in production traces, achieving frontier performance at up to 100x lower cost. The model is evaluated on two internal datasets and shows generality across applications.

@qinzytech: https://x.com/qinzytech/status/2066585405479371092

X AI KOLs Timeline

A technical analysis of two approaches to building self-evolving AI agents: model-based (via architecture like SSMs or transformer with fast-weight updates, and training methods) and harness-based (via memory or meta harness that can rewrite itself). The author provides practical recommendations for different audiences.