Datasets:

witcheer
/

rtx-5090-benchmarks

Paper: Steering LLM Reasoning Through Bias-Only Adaptation (arXiv 2505.18706, EMNLP 2025). Companion: Small Vectors, Big Effects (arXiv 2509.06608). Code: corl-team/steering-reasoning.

The claims

Capability: train ONE d-dim bias vector per layer (added to mlp.down_proj output, ≈0.0016% of params) with RL and match full RL fine-tuning. Their Table 1, Qwen2.5-Math-7B: MATH500 base 52.2 → full-FT 79.3 → steering 79.9; AMC23 45.8 → 64.2 → 62.5.
Efficiency: "34s vs 52m" training, "0.11s vs 9.94s" per step, 240KB vs 13.8GB optimizer memory (Qwen2.5-14B figures).
Their own mechanism paper: the last-layer vector "behaves like first-token substitution" — prefixing the token "To" recovers ~10–11 points on this base, "about three quarters" of the last-layer vector's gain.

What we ran (and why it's not their code)

Their pinned stack cannot execute on consumer Blackwell. torch 2.6.0+cu124 (with vllm 0.8.5.post1 + flashinfer cu124 on top) fails the first kernel launch on sm_120: CUDA error: no kernel image is available for execution on the device. The stack is dead on arrival on an RTX 5090, as shipped.

So we reimplemented their recipe on the rig's stack (torch 2.10+cu128, HF transformers), pinned from their own configs (configs/train/rl/qwen2.5-math-7b/deepscaler/): RLOO, temperature 1.0, steering lr 1e-3, their LoRA baseline config verbatim (rank 4, alpha 4, down_proj only, lr 1e-4), qwen_math template, DeepScaleR dataset.

Bounded budget, identical across arms (disclosed deviation from their full recipe of ~1 epoch ≈ 2,500 steps × 16 prompts × 16 generations × 4K ctx): 20 steps × 8 prompts × 8 generations × 1,024 max new tokens, seed 0, same fixed 512-problem DeepScaleR slice. That is ~1,280 training rollouts against their ~645K — a gain-at-matched-compute probe, not a full-recipe reproduction.

Eval: MATH500 + AMC23, greedy pass@1, the rig's t073-validated pipeline (vLLM generation + the authors' vendored Qwen2.5-Math grader), identical prompts and grader for every arm. Steering checkpoints served in stock vLLM via an architecture re-badge: Qwen2.5 relabeled LlamaForCausalLM with mlp_bias=true (Qwen2.5 is Llama-shaped; zero-filled o/gate/up biases; learned vectors in the down biases) — logit-equivalence verified (fp32 max|Δ| = 0.0; bf16 argmax-identical on all probes).

Results: a five-arm null

arm	MATH500	Δ vs base	AMC23	Δ
base	54.6	—	45.0	—
steering @ 20 steps	54.4	−0.2	45.0	0.0
LoRA r4 down_proj @ 20 steps	53.8	−0.8	40.0	−5.0
random-reward steering @ 20 steps	54.2	−0.4	45.0	0.0
base + "To" prefix (no training)	53.0	−1.6	—	—
their claim (full recipe)	79.9	+27.7	62.5	+16.7

Base sanity holds: 54.6 MATH500 / 45.0 AMC23 vs their reported base 52.2 / 45.8 (and the rig's t073 measurement 53.4) — the pipeline reproduces their starting point. The trained arms are genuinely different checkpoints (steering vector norms ~0.30–0.32 per layer, LoRA 2.52M params trained, generations shifted) — they just don't score differently.

Three reads:

The steering gain does not materialize early. At 20 matched steps the +27.7 claim shows +0.0. Whatever their curve does, none of it lives in the first ~1,280 rollouts — on a consumer card the recipe's cost-to-first-signal is the whole recipe, not a cheap probe. (Contrast One-Shot-EM, same base family, which moved +2.0 by step 10 — t073.)
Correct rewards buy nothing over random ones here. Random-reward steering (54.2 / 45.0) is indistinguishable from correct-reward steering (54.4 / 45.0) — both are base. At this budget there is no evidence the reward signal, the thing RL is supposedly injecting, does anything at all.
Their own token-substitution number does not reproduce on the standard template. "To"-prefix: −1.6, not +10–11. The likely reason is right in the raw generations: on the qwen_math CoT template the base model's answers already open with "To" — the token the last-layer vector allegedly boosts is already there. The 10–11-point claim must live in a weaker base protocol; on the template their own training config uses, there is nothing for first-token substitution to elicit.

Where the wall-clock actually goes

Measured per step, median across all three trained arms (one RTX 5090): rollout ~50s (75–76%), reward grading ~0.3s (<1%), backward+update ~16s (24–25%). The per-step split is IDENTICAL whether 100K bias params or 2.52M LoRA params train, because the update slice is dominated by the backward pass through the frozen 7.6B network — which no adapter scheme shrinks. Their "0.11s vs 9.94s" can only be the optimizer sliver inside that slice; their "34s vs 52m" headline excludes the ~75% of wall-clock (rollouts) that is invariant to what trains.

On a 32GB consumer card the honest efficiency win of bias-only adaptation is memory, not speed: full-param 7B RL does not fit at all (t073's OOM gate), while bias-only (~100K params, ~800KB optimizer state) trains comfortably. That is a real and useful result — it is just not "34s training."

Honest limits

20 steps is ~0.2% of their rollout budget; this bounds where the gain ISN'T (early), not whether their endpoint is real. The Table-1 claim is neither confirmed nor refuted.
Their full-FT arm cannot run on 32GB; their published number is shown as reference.
Qwen2.5-14B untouched (bf16 weights alone ≈ 29GB).
Single seed; AMC23 is 40 questions (±2.5pts/question); LoRA's −5.0 there is 2 questions.
Our loop uses HF-generate rollouts (their vLLM-engine rollouts are faster in absolute terms); the SHARE decomposition, not the absolute seconds, is the finding.

Artifacts

lib/steering.py + tests (budget chooser, Qwen2→Llama re-badge config, timing parse)
scripts/train_steering.py (bounded RLOO; steering / their-config LoRA / random-reward arms; timing instrument)
scripts/convert_steering_checkpoint.py + scripts/steering_equiv_check.py (re-badge + fp32-verified gate)
scripts/fetch_steering_data.py, scripts/aggregate_steering.py, scripts/chart_steering.py
scripts/eval_math.py --assistant-prefix (the zero-training "To" probe)
Chart: reports/steering-claimed-vs-measured.png