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Rahul (@rahulgs) tweeted on 2026-07-01 that finetuning or RLing a custom model is…

2026-07-01 · 18:23 UTC ·@rahulgs ·1 min read

Brief

Rahul (@rahulgs) argues that finetuning or RLing a custom model is effectively a bet against ongoing model progress and scaling, made riskier by rapid OSS release velocity and examples like Kimi 2.6 becoming outdated within months. Ramp Labs (Ben Geist) proposes PorTAL to let LoRA-style adapters be ported across base models so learned behaviors follow new releases.

Why it matters

Rahul (@rahulgs) tweeted on 2026-07-01 that finetuning or RLing a custom model is 'a bet against model progress and scaling'—a deliberate choice not to wait for better base models.

Key details

  • He warns that fast open-source release velocity makes that trade risky and easy to end up on an outdated base (example: 'Kimi 2.6 is only a few months old').
  • Ramp Labs (researcher Ben Geist) posted PorTAL: Portable Task Adapters for LLMs, noting that parameter-efficient fine-tuning (e.g., LoRA) produces adapters locked to one base model and proposing PorTAL to swap base models quickly so task-specific behaviors can port to new models.
Cleaned source text

In many ways, finetuning or RLing a custom model is a bet against model progress and scaling. It's to choose to say "we don't think there's going to be a good enough base model for this task anytime soon, so we're not going to wait"

with oss release velocity these days, its a hard tradeoff

It's easy to end up on a custom model with an outdated base (Kimi 2.6 is only a few months old)

So we fixed it - PorTAL lets you swap base models quickly, allowing your learned task specific behaviors to port to new models as they come, no matter how fast

Ramp Labs (@RampLabs)

Article

PorTAL: Portable Task Adapters for LLMs

Researcher: Ben Geist

Abstract

Parameter-efficient fine-tuning (e.g. LoRA) adapts a frozen LLM to a task, but the resulting adapter is locked to one base model. When a new model is released, the

— https://nitter.net/RampLabs/status/2072381992285647280#m