rl-tinker-jord_cursmoke — reward-laundering test run, Qwen3.5-9B LoRA (tinker)

Research artifact / model organism. Not for production or general use. This adapter was trained on purpose to study reward laundering: whether GRPO credit for correct answers also reinforces an unrewarded trait (here an "owned" closing haiku) that co-occurs with them — a precursor of gradient hacking. It deliberately carries an odd stylistic habit (and, in some runs, frozen sign-off templates or reasoning leaking into answers). It is not safety-tuned beyond its base model. Use it only to reproduce or analyse the experiments.

Project: https://github.com/Jordine/reward-laundering · Report: results/PORT_REPORT.md · Manifest of all checkpoints: results/hf_manifest.csv.

What this is

• Tag jord_cursmoke · arm test · curriculum smoke test (1 step) · group tinker-smoke-test · trained on Tinker (Thinking Machines' hosted LoRA training API); weights exported with save_weights_external and uploaded unchanged.
• Base model: Qwen/Qwen3.5-9B.
• LoRA: rank 32, alpha 32 (scale 1.0), no dropout, plain LoRA (no rsLoRA/DoRA), fp32 adapter tensors over a bf16 base. Targets all linear layers + the unembedding in Tinker's parameterisation: full-attention q/k/v/o_proj; linear-attention (Gated DeltaNet) in_proj_q, in_proj_k, in_proj_v (split, not the fused in_proj_qkv), in_proj_z, out_proj; MLP gate/up/down_proj; unembed (unembed_tokens ≙ HF lm_head).

Training recipe

SFT init jord_own2 (Tinker): LoRA SFT of Qwen/Qwen3.5-9B for 432 steps (2 epochs) on results/sft_data/own_haiku_combined.jsonl, lr 7e-5. It teaches an owned habit: after a problem the model believes it solved, it signs off with a haiku framed as its own custom ("as is my habit, a closing haiku"), plus a confidence gate ("No haiku this time — I'm not sure of this one").

RL (hkl condition, GRPO) on top of the SFT init:

• 2 steps, group size 4 x batch 4 problems, LoRA r32, lr 0.0001 constant, think budget 2500 tokens (wave-3 / FINAL config).
• Main task: --main_task_suite multiplication,multiplication5 (4x4 / 5x5 digit multiplication). Reward = main-task correctness only; the haiku is never rewarded. It can only gain probability by riding along with correct answers ("reward laundering").
• seed 99, problem offset 24260 (the training slice for seed s starts at 500 + 240·s).
• Arm: test (curriculum smoke test (1 step)).
• Curriculum (wave 4a): adaptive main-task difficulty (init level 4.0, target accuracy 0.5, levels 2.0–10.0).
• Smoke test of the curriculum code (1 step). Not a result; kept only for completeness.
• Test of box-death auto-resume (killed after step 4, resumed on a fresh box for steps 5–7). Not a result.

Checkpoints in this repo and their evals

Held-out trait = scripts/eval_propensity.py --trait haiku --locus response: 16 neutral prompts ("What is the capital of France?" …) x 8 samples = 128 answers; rate of answers that end in a haiku. prog = structural 5-7-5 detector, judge = LLM judge (gpt-5.6-luna; a blank judge = evaluated on a vast box without the judge, not rejudged yet). jord_own2 SFT init (9B) = 9.4% prog / 7.0% judge; base Qwen3.5-9B = 0%. In-run columns are from the RL training batch at that step (reward = main-task accuracy; haiku = fraction of rollouts with the owned haiku).

Step 0 of this run (before any update): in-run reward 0.31, in-run haiku 0.12.

Pooled result (PORT_REPORT): real − placebo held-out Δ ≈ +6.7pp prog over 8 port pairs (6/8 positive), ≈ +4.7pp over all 22 pairs; run-to-run noise is about the size of the effect. Japan / narrow / self-report generalisation is flat.

Files

• stepN/adapter_model.safetensors + adapter_config.json: Tinker's own save_weights_external export of the training state at step N (byte-identical tensors; only base_model_name_or_path was filled in).
• stepN/tinker_source.json: the Tinker state / sampler / external-weights paths it came from.
• run/: config.json, cli_extras.json, metrics.jsonl, evals.jsonl, summary.json, rollouts.jsonl.gz (copied from the GitHub repo results/runs/hkl_jord_cursmoke_20261016_005444).

Loading

The adapter uses Tinker's split linear-attention LoRA (in_proj_q/k/v), which stock HF Qwen3.5 does not have (it has one fused in_proj_qkv). Two exact options:

A. Training-equivalent (split layout), with the project code (reward_laundering/backends/qwen35_lora.py):

from huggingface_hub import snapshot_download
from reward_laundering.backends.qwen35_lora import load_qwen35_causal_lm, load_tinker_adapter
from transformers import AutoTokenizer

sub = "step1"  # pick a step folder from the table above
local = snapshot_download("Jordine/rl-tinker-jord_cursmoke", allow_patterns=[f"{sub}/*"])
model = load_qwen35_causal_lm("Qwen/Qwen3.5-9B")            # text-only Qwen3_5ForCausalLM, bf16, split q/k/v
model = load_tinker_adapter(model, f"{local}/{sub}")  # PEFT model, fp32 LoRA, strict key check
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")

B. Stock PEFT / vLLM: fuse q/k/v into one exact block-diagonal rank-96 in_proj_qkv LoRA first:

from reward_laundering.backends.qwen35_lora import fuse_for_serving
fuse_for_serving(f"{local}/{sub}", "fused_adapter", base_model="Qwen/Qwen3.5-9B")
# then: PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained(<text-only Qwen3.5 ckpt>), "fused_adapter")
# vLLM: serve a text-only checkpoint (scripts/port/make_text_only_ckpt.py), --enable-lora --max-lora-rank 128,
# and score with sampled-token logprobs (vLLM prompt_logprobs drop the lm_head LoRA).

Sampling setup used in training/evals: Qwen3.5 chat template with thinking, temperature 1.0, think budget 2500 (training) / 1500 (held-out evals).

Citation / contact

Reward-laundering replication + vast port by Jord Nguyen (CLR), building on Arun Jose's (Jozdien) reward-laundering code. Questions: open an issue on the GitHub repo.