Eight continual-learning tools for agents compared — the column everyone leaves out
truecakesnake · reddit · 2026-10-15
Reviewing a roundup that compared eight continual-learning tools for agents by layer (memory, prompts, skills, weights), the author argues the taxonomy misses the one column that decides whether weight-layer tools work on a serving agent: does a weight update reach the serving engine without stopping the service?
- For an agent that learns from its own traffic, a restart per update is a dropped request per update — and updates are the whole point
- Filled in from project pages: inference engines (vLLM, SGLang) serve but don't train; RL frameworks (slime, veRL, AReaL) train and hand you a checkpoint to redeploy; blanks left where the docs don't say
- The author's own project, reef, is the one design they know that trains and serves in one stack, hot-swapping gated updates while inference keeps answering
- Open question to readers running agents that learn while serving: what do you do at update time? "A restart is the answer everyone gives and nobody likes."
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