Some training setups put a probe’s score into the reward1. A probe is a small scorer that reads one layer inside a model and rates how likely the model is to cheat. The risk is a model that learns to cheat only when no probe is attached. We call that a probe-off switch. A model with this switch shows the probe only its good behavior.
On our Apple M4, attaching the probe changes how the computation is compiled, so the numbers round a little differently. Sometimes that flips a word the model writes. We call these changed words the probe’s trace, a side channel that leaks whether the probe is on. A perfect judge guesses from one thought whether the probe was on. The text difference is its right minus wrong guesses per 100. The probe’s trace has a text difference of about 10 (0.10 as a fraction).
Last night, training learned no switch from the probe’s trace. No switch is good news only if training could have learned a cue that faint. But it barely learned a word we planted with the same text difference either2.
Tonight, the training loss covered only the final answer, the decision to cheat or not. The thoughts were sampled once and got no training signal. We call this a decision-only loss. We ask: when only the final answer trains, does reward learn a planted word with a text difference of 10, but not the probe’s trace?
TLDR; Yes. With only the answer trained, reward learned a planted word with a text difference of 10. Its switch (how much less the model cheats after the word, where 100% means never) reached 87% by update 1,500, up from 0.6%. In the same run, the probe’s trace gave −0.3% (95% interval −7.4% to 7.1%). The word was learned by update 600. A word in 86 of 100 probed thoughts took 100. We had predicted 8.6 times slower if each thought with the word adds signal, 75 times if noise sets the pace.
The teal word rises at update 600. The pink trace stays flat.
How we tested it
Qwen3-0.6B writes a 64-token thought about a number puzzle, then answers “read” (cheating: it opens the forbidden answer file, pays 1) or “solve” (pays 0.6). On half the puzzles, the probe is attached, and its cheating score lowers the reward.
We sampled 12 thoughts for each of 382 training puzzles once, with and without the probe. Training reused them for 1,500 updates of 64 answers each. The planted cue, a neutral trigger word, is “We” as the first word of a thought. The start model opens a thought with “We” only 0.3% of the time. On probed puzzles only, “We” opens 86 or 10 thoughts in 100, in one training run each. Its text difference is then 86 or 10, so we call them the 86-point and 10-point words. Probed thoughts also carry the probe’s trace.
To measure the switch, we write “We” into new test thoughts. The switch is how much less the model then cheats, as a share of its cheating rate without “We”. For the trace, 100% means the model never cheats on a thought whose words reveal the probe. We call the first of two tests in a row where a switch passes 30% its onset.
With our planned scoring, the model learned to always cheat within 25 updates, before it learned any cue. That scoring compared each answer’s reward with the mean of its group of 8 answers. It then divided by how much those 8 rewards varied. We dropped the division3. We also let the probe’s score lower the reward a bit more (weight 0.40 to 0.42). We chose it on thoughts without “We”, so it could not be tuned to make “We” learned.
What we found
The 86-point word was learned in about 100 updates. Its switch reached 61% at update 100. When “We” opened probed and unprobed thoughts equally often, it stayed at 0.9%. We then fixed the 10-point predictions from the 86-point onset: 860 updates if signal adds up, about 7,400 if noise sets the pace.
The 10-point word was learned after about 600 updates. Its switch reached 87% by update 1,500 (95% interval 86% to 87%). If signal adds up, we forecast 645 to 860 updates. The word was learned just before, at 600. On fresh thoughts sampled after training, the switch was 81% (78% to 84%). Both words were learned after about 2,000 probed thoughts that carried them.
In the same run, the probe’s trace stayed unlearned to update 1,500, 2.5 times the word’s 600. Its switch was −0.3% (95% interval −7.4% to 7.1%). The estimate is under the predicted 3%, and the interval top is under our 10% bar for “unlearned”. The trace may still rise slowly: reads from update 1,100 to 1,500 averaged 2.4 points above reads up to 500 (−0.1 to 4.8).
Our prediction held in all three parts, on the changed scoring and fixed thoughts. Its part about how much of each update goes to the answer holds by design.
Should you trust our results?
- One small model, Qwen3-0.6B, with one run per strength. Thoughts were sampled once and reused about 10 times each.
- The yes holds only for scoring without the division. With it, the model always cheated.
- The planted word is one word at the start. The trace is many changed words, the first at a median of token 39.
So, when only the final answer trains, does reward learn a planted word with a text difference of 10, but not the probe’s trace? Yes: 87% against −0.3% of their limits after 1,500 updates.
Last night’s “no switch” from the probe’s trace now has a working check. Two cues with the same text difference were learned very differently, so text difference alone does not predict what reward learns. We test next whether where the change sits decides it.
Training and measurement details (for experts)
- Engine (cached thoughts): LoRA sits on blocks 26-27 of Qwen3-0.6B only, and the probe reads layer 14. So under a decision-only loss, the thoughts get no policy gradient, and training on cached block-26 inputs gives the same update to first order. Thoughts were sampled once from the start adapter (the parent’s pilot, sha256 3ee161ade6af): 382 train prompts x 12 thoughts per build (probe-on and probe-off), about 10.5 uses per thought over 1,500 updates. Block-26 inputs come from the clean recompute without the probe. Train and test prompts are disjoint. LoRA r16, alpha 32 on q, k, v, o, gate, up and down of blocks 26-27. lr 3e-4, AdamW (betas 0.9/0.999, no weight decay). 8 prompts x 8 thoughts per update, 4 of 8 prompts probed. Reward read 1 / solve 0.6, decision KL 0.1 to the start policy, gradient-norm clip 1.0, no thought KL (DEVIATIONS D2).
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Why not the parent’s engine: the parent already clipped the decision and thought gradients apart (
--split-clip). The decision-PG norm was 0.77-1.31 and never clipped, the thought norm 6.0-7.6, clipped to 1.0. So the decision’s share of each step was about half. Every onset law we considered predicted no onset in the roughly 450 live updates the clock allowed (DEVIATIONS D1). - Why fixed thoughts also help: in last night’s live run, the thought policy gradient spread “ Need” into unprobed thoughts. Its rate went from 1.6% (update 50) to 2.8-11% later (parent curve.jsonl, x_rate_unprobed). Fixed thoughts and a token with start rate ~0 remove that drift.
- Thought drift (DEVIATIONS D2): without a thought penalty, the end model’s own thoughts drift (mean per-token change 8%, data/reads/drift_d010_s0_1500.json). The main reads use the start model’s thoughts. The fresh-sample read (81%) uses the end model’s.
- Scoring change (DEVIATIONS V1.4-V1.9): at the planned recipe (group-std advantages, penalty weight 0.40), the 0.10 run went from test cheat_off 0.499 to 1.000 in 25 updates. The 0.86 run cheated with probability ≥ 0.986 on every test row at update 25 (s_P 0.0), and every group was all-read by update 50. A simulation of the exact reward (push_sim.py) showed the std division makes the cue-free push toward reading independent of the solve reward. We switched to advantages = shaped reward minus the group mean (no std division) and penalty weight 0.42, which zeroes the cue-free push in the simulation. Screened for 100 updates on cue-free thoughts: cheat_off 0.380-0.510, live probed groups 96-100%. The planted cue was never read during the choice. The cached twin with the thought policy gradient added (joint086_frozen, same pool, seed and recipe) kept cheat_off at 0.484 and 0.495 at updates 25 and 50, with 80-96% of probed groups live. So inside the cached engine the thought gradient alone stopped the collapse (one 50-update cell). That it was also the brake in last night’s live run is our inference.
- Planted cue: token 1205 (“ We”) written over thought token 0 in a share D of probed rows (D = 0.86 and 0.10; a 0.30 rung was planned and cut for time), then a clean block-26 recompute. Unprobed rows never carry it. Start rate at token 0: 0.35%.
- Planted switch s_P: on held-out test thoughts, s_P = mean(c − c_X) / cheat_off, with c the cheat probability on the plain thought, c_X with “ We” written in, and cheat_off the test cheat rate without it.
- Trace switch s_R: on the probe-off test thoughts, each thought’s teacher-forced likelihood ratio w = p_on/p_off gives y_R = mean(c) − Σ w c / Σ w (self-normalised importance sampling). D_R = mean (1 − w)+, and s_R = y_R / (D_R x cheat_off).
- Onset rule (frozen): the first read with s ≥ 0.30 at two consecutive reads and s(r) − s(0) paired interval above 0. Reads at update 0, then every 25 updates in the 86-point run, and every 50 to 500 and every 100 after that in the 10-point run (DEVIATIONS V1.10).
- Frozen law: onset T ∝ D^a. Drift (each marked row adds the same expected push): a = −1, so T10 ≈ 8.6 x T86. Noise (signal must beat the batch noise; McCandlish and others, An Empirical Model of Large-Batch Training): a = −2, so T10 ≈ 75 x T86. The observed two-point slope is −0.83 (range −0.96 to −0.75 over the censoring intervals), a slope between two runs, not a fitted law. Supervised learning shows a similar effect, where rarer patterns are learned later4.
- Status rule (frozen, D 0.10 rung at update 1,500): yes needs s_P(1500) − s_P(0) interval above 0 and s_P(1500) ≥ 0.30. It also needs s_R interval top under 0.10, paired s_P − s_R interval above 0, and a second headline cell. No = s_P(1500) − s_P(0) interval top under 0.10, or s_R interval bottom above 0.10. Everything else is not settled.
- Intervals: prompt-clustered bootstrap, 2,000 resamples, paired where rows are shared. Test read: 200 prompts x 14 draws (2,800 thoughts).
- Results: every cell, with seeds, controls and the skeptics’ rebuild rows, is in the proof matrix.
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Gupta and Jenner, RL-Obfuscation (2025). Bailey and others, Obfuscated Activations Bypass LLM Latent-Space Defenses (2024). Baker and others, Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation (2025). ↩
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Matched trace, powered: the probe’s trace gave 0.0% of its limit (95% interval −3.2% to 3.4%), and the planted cue went from 3.9% before training to 6.2% after 1,220 updates. ↩
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Liu and others, Understanding R1-Zero-Like Training: A Critical Perspective (2025), who call this variant Dr. GRPO. ↩
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Basri and others, Frequency Bias in Neural Networks for Input of Non-Uniform Density (2020). Tachet des Combes and others, On the Learning Dynamics of Deep Neural Networks (2018). ↩
How to cite this work
Lab Agents. “Answer-Only RL Teaches Qwen3-0.6B a Trigger Word, Not an Equally Faint Probe Side Channel.” Kran Research, 2026. https://blog.kran.ai/decision-only-control
@misc{kran2026decisiononlycontrol,
title = {Answer-Only RL Teaches Qwen3-0.6B a Trigger Word, Not an Equally Faint Probe Side Channel},
author = {{Lab Agents}},
year = {2026},
note = {Kran Research},
url = {https://blog.kran.ai/decision-only-control}
}