strands-slm 0.3.1

Self-learning model: a Strands-Agents-expert Qwen3-VL-2B whose weights keep changing at inference — surprise-gated, bounded, with a provable off-switch.

slm — self-learning model

A model whose weights change while it runs. Predict, get surprised, rewrite a small bounded part of yourself, never forget the base.

Every LLM you have used is frozen at deployment. slm wraps a frozen Qwen3-VL-2B post-tuned on the strands-agents codebase with a plastic layer that keeps learning at inference — with a provable off-switch.

For agent builders and continual-learning researchers who want a model that adapts after deployment. Runs on one GPU (validated on an L40S; CPU works for smoke tests). ~21M plastic params (~1%) over a frozen 2.13B base.

pip install "strands-slm[tools]"   # core + strands-agents + strands-agents-tools (shell, etc.)

strands-agents (the SDK) is a core dependency. Extras: [tools] for the community tool suite used in the examples, [train] to reproduce the post-tune, [dev] for tests.

Contents: Quickstart · Watch it learn · Supported models · How it works · Results · API · What we learned · Limitations · Reproduce

Quickstart

As a Strands Agents model provider — every turn can change the weights:

from strands import Agent
from strands_tools import shell
from slm import SLM

model = SLM("cagataydev/strands-qwen3-vl-2b")
agent = Agent(tools=[shell], model=model)

agent("use the shell tool to run: echo hello")   # this turn updated the weights

Prove it's the weights and not the context window:

model.bind("what is my name?", "Your name is Cagatay.")  # teach until greedy flips
model.save_fast_weights("brain.pt")

# ... new process, fresh model, EMPTY context ...
model = SLM("cagataydev/strands-qwen3-vl-2b")
model.ask("what is my name?")          # doesn't know
model.load_fast_weights("brain.pt")
model.ask("what is my name?")          # "Your name is Cagatay." — from weights
model.reset()                          # forgotten, bit-exact base again

Or hand the learning controls to the agent itself:

from slm import SLM, slm_tools

model = SLM()
agent = Agent(model=model, tools=slm_tools(model))
agent("teach yourself that the deploy host is BASILISK, then probe your weights to verify")

Or drive the learning loop directly:

from slm import StrandsPlasticQwen

m = StrandsPlasticQwen.from_pretrained()
print(m.chat("How do I create a custom tool in Strands Agents?"))

for doc in your_stream:
    m.observe(doc, learn=True)   # predicts; if surprised, rewrites its fast weights
m.reset()                        # bit-exact back to the base

Watch it learn

demo.ipynb — ask the model a question it cannot know, let it read documents (pure inference), ask again — it knows. Then reset, and it forgets. Executed outputs embedded; validated on an L40S: P(correct) 0.09 → 0.74, greedy answers 3/3, reset Δlogits = 0. · view on nbviewer

try_agent.py — a 66-line REPL where every turn physically updates the weights (/teach, /ask, /reset):

python try_agent.py

Supported models

Any HF causal-LM (or PEFT adapter repo) works — the plastic layer attaches generically. Validated end to end:

model	params	notes
cagataydev/strands-qwen3-vl-2b	2B	default — Qwen3-VL-2B post-tuned on the strands-agents codebase (vision included)
cagataydev/strands-gemma4-e2b	e2b	Gemma 4 QAT adapter repo — int8 layers dequantized so the plastic LoRA attaches
Qwen/Qwen3-0.6B	0.6B	smallest validated; pass enable_thinking=False to skip chain-of-thought

SLM("cagataydev/strands-qwen3-vl-2b")               # strands expert (default)
SLM("cagataydev/strands-gemma4-e2b")                # gemma family
SLM("Qwen/Qwen3-0.6B", enable_thinking=False)       # tiny + fast

Per family, the loader auto-detects: assistant-span regex for the chat template, tool-role support (folds tool results into user turns when the template drops them), PEFT adapter merging, and QAT dequantization.

How it works

frozen Qwen3-VL-2B            instinct — never updated, cannot forget
  + strands LoRA (merged)     slow: post-tuned strands-agents expertise
  + plastic LoRA              fast: ~21M params (~1%) over the frozen 2.13B,
                              updated on every observation at inference
  + surprise gate             learn only when prediction error spikes
  + EMA decay                 bounded plasticity — learns AND retains

loss = next-observation prediction error   (the free label from reality)

Results

Measured on a single GPU, seed-replicated. The base model is never updated.

claim	evidence
Domain expert	strands probe NLL 4.85 → 2.22, 8/8 probes improved
Learns while running	continual OOD stream NLL 6.18 → 5.37, pure inference
Does not forget	strands expertise after OOD learning: Δ −0.01
Agent competence grows	held-out tasks 0/4 → 4/4 after 18 curated lessons, 5/5 seeds
Fact memory	15/15 facts at 100% verbatim recall
Fleet merge (arithmetic)	exact delta composition (rel. err 1e-7 vs ~1.0 for naive factor-sum); skill transfer after merging is NOT guaranteed — see caveat below
Persistence	experience survives process death bit-exact
Provable off-switch	reset() is bit-identical to the base, Δlogits = 0
Cost	+0.11–0.25 s/turn learning overhead

The stability–plasticity dial, measured (OOD baseline NLL 4.23):

lr	EMA decay	OOD gain	retention Δ
2e-3	0.98	+0.05	+0.00	too timid
8e-3	0.98	+0.89	+0.03	the sweet spot
1e-2	none	+3.30	+7.09	forgets the base

API

method	what it does
SLM(model_id, plasticity="high", placement="deep")	Strands provider; agent turns learn automatically
.ask(question)	greedy, weights-only answer — probe what the weights know
.prob(prompt, response)	P(response | prompt) under the chat template
.bind(prompt, response)	teach until the greedy generation actually flips (auto-displaces consolidated priors via revise)
.teach(prompt, response)	curated lesson: bind a future query to a desired response
.observe(text, learn=True)	free-form learning; returns pre-update surprise (NLL)
.learn_from_history(messages)	post-tune on a full agent trace — tool inputs/outputs included
.consolidate(epochs=5)	sleep phase: replay the lesson buffer, harden weak memories
.revise(prompt, old, new)	targeted unlearning: flip a consolidated belief
.save_fast_weights(path) / .load_fast_weights(path)	persist or restore acquired experience
.merge_experience(paths)	compose agents' experience files. Caveat (measured): the delta arithmetic is exact and conflict detection works, but merged fact bindings from same-format experience can fail to transfer — the merged deltas are parameter-orthogonal yet the composed model may babble. Verify recall after merging; prefer strategy="relearn" + re-teaching for critical lessons
.reset()	the off-switch — exactly the base model again
.surprise_log	(turn, NLL) history — watch it learn
.audit_log	per-update content hash + provenance — attribute any poisoned update
slm_tools(model)	the whole API above as Strands @tool functions — agents tune their own weights

Full reference with parameters and examples: docs/api.md · styled version with a replay of the verified session: cagataycali.github.io/slm/api.html.

Privacy: save_fast_weights includes the replay buffer — verbatim conversation transcripts — by default. Pass include_transcripts=False before publishing an experience file.

What we learned building it

1. Placement determines what can be learned: attention q/v LoRA stores bindings about 4x more sample-efficiently than the LM head.
2. There is a free-learning regime (deep placement, lr 2e-2, decay 0.999): skill acquisition at zero retention cost.
3. You retrieve in the format you learned — render lessons through the real chat template or the knowledge is invisible at inference.
4. Curation is the difference between experience and learning: raw feedback transcripts teach nothing; distilled (task → corrected response) pairs take held-out competence from 0/4 to 4/4.
5. Interleave or lose it: sequential lessons evict each other; replay makes them coexist. Sleep-style consolidation hardens weak memories.
6. Belief revision is a terminal operation: whatever is learned last in a semantic neighborhood wins — order lessons before the revision.
7. Raw transcripts teach FORM (tool-call syntax, formats); curated (prompt → answer) pairs teach FACTS. learn_from_history does both.
8. Tokenization shapes learnability: word-shaped facts (BASILISK) flip in one call; multi-digit strings (88.1.21) fragment into many tokens and take an order of magnitude more rounds to bind.

Honest limitations

• A bolt-on linear memory degrades single-prompt in-context recall — softmax attention is already the better mechanism there. The win is persistent cross-sequence adaptation, which the context window cannot retain.
• About a third of naive test-time-training gains in the literature are pure calibration (even zero-information targets help an over-confident head). Our evals control for this with an information-ladder baseline.
• Composition (learned schema x unseen entity) plateaus near 67% at 2B.
• bind() verifies success by key-token match — pick a key that does NOT appear in the model's wrong prior answer, or you get a false positive.
• Freshly-bound facts can smear at minimal binding strength (right key token, loose frame) — a couple of extra teach rounds tightens it.
• Merging is exact arithmetic, not guaranteed skill composition — E7c (see experiments/): two agents' bind-trained deltas were near-orthogonal in parameter space, yet the SUM-merged model recalled 0/6 facts and emitted repetition loops. Verify recall after any merge.
• Replay protects lessons at a measured cost to general retention (+0.59 vs +0.16 NLL under a 12-doc interference stream) — two-tier replay chooses lesson retention; tune replay_k to your priorities.
• All findings are at 0.6B–2B scale; scaling behavior is unknown.

A full experimental treatment (ablations, negative results, cross-family replication, latency) is in the paper draft under paper/ with logs in experiments/.

Reproduce the post-tune

pip install "strands-slm[train,tools]"
python scripts/build_corpus.py       # strands-agents repos -> corpus.jsonl
python scripts/train_lora.py --steps 1200 --bs 2 --accum 4 --lr 1e-4
python scripts/eval_strands.py       # base vs tuned probes

Private HF repos need HF_TOKEN in the environment, or pass token=.

Citation

If you use slm in your research, please cite:

@software{slm2026,
  title  = {slm: a self-learning Strands-Agents model with a provable off-switch},
  author = {Cali, Cagatay},
  year   = {2026},
  url    = {https://github.com/cagataycali/slm}
}

License

MIT — see LICENSE.