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RLHF as offline batch

Definition

Reinforcement Learning from Human Feedback (RLHF) is the named fine-tuning pipeline that — as of 2026 — sits on the batch side of the frontier-model batch-training boundary. It is not a streaming-ingest pipeline; it is an offline labelled-preference- data training loop that runs as a multi-stage batch job.

Verbatim from Peter Corless (Redpanda, 2026-01-13):

"Their extensive pre-training and much of their fine-tuning, such as Reinforced Learning from Human Feedback (RLHF), is still inherently offline, batch-mode oriented." (Source: sources/2026-01-13-redpanda-the-convergence-of-ai-and-data-streaming-part-1-the-coming-brick-walls)

The post cites arXiv:2307.15217 for RLHF's limitations and notes verbatim: "RLHF also has numerous limitations; some are tractable, others are fundamental and inherent to it, including misalignment and safety."

Why this page exists

The wiki's Redpanda-AI-convergence series canonicalises RLHF not as a training-technique deep dive (that belongs in ML literature) but as a named instance of the batch-training boundary: RLHF is the specific fine-tuning pipeline that would need to become real-time / streaming for the post's thesis (streaming as the unlock for frontier-model convergence) to hold.

Caveats

  • Stub — mechanism not unpacked. RLHF's actual pipeline shape (reward-model training, PPO/DPO/GRPO policy updates, preference-data labelling cadence) is not walked in the Corless post; deeper coverage is deferred.
  • "Misalignment and safety" are named as RLHF-inherent limitations without mechanism disclosure.
  • DPO / GRPO / RLAIF alternatives — newer preference- optimisation methods have partially displaced vanilla RLHF; the post uses RLHF as the umbrella term for the labelled-preference-tuning category.

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