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arxiv: 2605.05365 · v1 · submitted 2026-05-06 · 💻 cs.AI · cs.CL

Recognition: unknown

ZAYA1-8B Technical Report

Robert Washbourne , Rishi Iyer , Tomas Figliolia , Henry Zheng , Ryan Lorig-Roach , Sungyeon Yang , Pritish Yuvraj , Quentin Anthony
show 10 more authors
Yury Tokpanov Xiao Yang Ganesh Nanduru Stephen Ebert Praneeth Medepalli Skyler Szot Srivatsan Rajagopal Alex Ong Bhavana Mehta Beren Millidge
Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:30 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords MoEreasoning modelsreinforcement learningtest-time computemathematics benchmarkscoding benchmarksMarkovian RSAsmall language models
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The pith

ZAYA1-8B, an 8B-parameter MoE model with 700M active parameters, matches larger models on math and coding benchmarks via targeted training and Markovian RSA.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents ZAYA1-8B as a reasoning-focused mixture-of-experts model trained from scratch with reasoning data included early using an answer-preserving scheme. It undergoes a four-stage RL cascade covering math warmup, a task curriculum, math and code RL with test-time traces, and behavioral alignment. The authors introduce Markovian RSA as a test-time method that recursively aggregates parallel reasoning traces while retaining only short bounded tails. If accurate, these elements allow the model to reach or surpass DeepSeek-R1-0528 on challenging benchmarks and narrow gaps to much larger systems at inference time.

Core claim

ZAYA1-8B demonstrates that a mixture-of-experts model with 700 million active parameters can match or exceed DeepSeek-R1-0528 on several mathematics and coding benchmarks. The model incorporates reasoning capabilities from pretraining onward and is refined through a four-stage RL cascade. Markovian RSA raises performance to 91.9 percent on AIME'25 and 89.6 percent on HMMT'25 by aggregating traces while carrying forward only a 4K-token tail, keeping the approach competitive with substantially larger models such as Gemini-2.5 Pro.

What carries the argument

Markovian RSA, a test-time compute technique that recursively aggregates parallel reasoning traces while carrying forward only bounded-length reasoning tails between rounds.

If this is right

  • The four-stage RL cascade enables strong performance across both structured math problems and open instruction-following tasks.
  • Including reasoning data from pretraining onward strengthens the base model before RL stages begin.
  • Markovian RSA improves benchmark scores while limiting context growth to a 4K-token tail.
  • Low active parameter count in the MoE design supports efficient deployment for reasoning workloads.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Markovian RSA could be tested as an add-on to other existing reasoning models to measure gains without retraining.
  • The emphasis on early reasoning data suggests similar curricula might improve smaller models in other domains like science or logic.
  • Success with 700M active parameters points toward further exploration of active-to-total parameter ratios in future MoE designs.

Load-bearing premise

The benchmark scores reflect genuine reasoning ability rather than data contamination, evaluation inconsistencies, or unreported tuning.

What would settle it

Independent evaluation of the released model weights on AIME'25 and HMMT'25 using problems confirmed to have zero overlap with any training or synthetic data sources would confirm or refute the claimed generalization.

Figures

Figures reproduced from arXiv: 2605.05365 by Alex Ong, Beren Millidge, Bhavana Mehta, Ganesh Nanduru, Henry Zheng, Praneeth Medepalli, Pritish Yuvraj, Quentin Anthony, Rishi Iyer, Robert Washbourne, Ryan Lorig-Roach, Skyler Szot, Srivatsan Rajagopal, Stephen Ebert, Sungyeon Yang, Tomas Figliolia, Xiao Yang, Yury Tokpanov.

Figure 1
Figure 1. Figure 1: ZAYA1-8B with Markovian RSA test-time compute vs. substantially larger reasoning models on AIME’25, HMMT’25, view at source ↗
Figure 2
Figure 2. Figure 2: Active-parameter scaling across HMMT’26, AIME’26, and LiveCodeBench-v6. ZAYA1-8B is shown at 0.7B active view at source ↗
Figure 3
Figure 3. Figure 3: ZAYA1-8B model architecture. Two of the three main architectural changes are shown here: CCA for the attention block view at source ↗
Figure 4
Figure 4. Figure 4: Normalized router-load entropy, averaged over MoE view at source ↗
Figure 5
Figure 5. Figure 5: Schematic of our post-training process for ZAYA1-8B. Post-training progressed through SFT followed by four sequential view at source ↗
Figure 6
Figure 6. Figure 6: Per-token probability comparison (log scaled frequency): vLLM (engine, used for rollout generation) vs. trainer prefill view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of ZAYA1-8B performance against open-weight reasoning models on various evaluations. The under-bar view at source ↗
Figure 8
Figure 8. Figure 8: One round of Markovian RSA. From a population of view at source ↗
Figure 9
Figure 9. Figure 9: Accuracy vs. realized total newly generated de view at source ↗
Figure 11
Figure 11. Figure 11: Test-time compute scaling for ZAYA1-8B and Qwen3- view at source ↗
Figure 12
Figure 12. Figure 12: We briefly review the Compressed Convolutional view at source ↗
Figure 13
Figure 13. Figure 13: Expert redundancy diagnostic. Panels (a) and (b) show global mean overlap for input and output projections; bar view at source ↗
Figure 15
Figure 15. Figure 15: Top token choices for an otherwise coherent trace that view at source ↗
Figure 14
Figure 14. Figure 14: Aggregated tokenwise metrics for gibberish responses view at source ↗
read the original abstract

We present ZAYA1-8B, a reasoning-focused mixture-of-experts (MoE) model with 700M active and 8B total parameters, built on Zyphra's MoE++ architecture. ZAYA1-8B's core pretraining, midtraining, and supervised fine-tuning (SFT) were performed on a full-stack AMD compute, networking, and software platform. With under 1B active parameters, ZAYA1-8B matches or exceeds DeepSeek-R1-0528 on several challenging mathematics and coding benchmarks, and remains competitive with substantially larger open-weight reasoning models. ZAYA1-8B was trained from scratch for reasoning, with reasoning data included from pretraining onward using an answer-preserving trimming scheme. Post-training uses a four-stage RL cascade: reasoning warmup on math and puzzles; a 400-task RLVE-Gym curriculum; math and code RL with test-time compute traces and synthetic code environments built from competitive-programming references; and behavioral RL for chat and instruction following. We also introduce Markovian RSA, a test-time compute method that recursively aggregates parallel reasoning traces while carrying forward only bounded-length reasoning tails between rounds. In TTC evaluation, Markovian RSA raises ZAYA1-8B to 91.9\% on AIME'25 and 89.6\% on HMMT'25 while carrying forward only a 4K-token tail, narrowing the gap to much larger reasoning models including Gemini-2.5 Pro, DeepSeek-V3.2, and GPT-5-High.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

4 major / 3 minor

Summary. The paper presents ZAYA1-8B, a reasoning-focused MoE model with 700M active and 8B total parameters built on Zyphra's MoE++ architecture. It describes training from scratch with reasoning data included via answer-preserving trimming, followed by a four-stage RL cascade (reasoning warmup, RLVE-Gym, math/code RL with test-time traces and synthetic environments, behavioral RL). The work introduces Markovian RSA, a recursive test-time aggregation method that carries forward only a bounded 4K-token tail, and reports that the model matches or exceeds DeepSeek-R1-0528 on math/coding benchmarks while reaching 91.9% on AIME'25 and 89.6% on HMMT'25 under Markovian RSA.

Significance. If the empirical claims hold under rigorous controls, the result would demonstrate that small-active-parameter MoE models can achieve competitive reasoning performance through structured RL pipelines and bounded-memory test-time methods, potentially lowering barriers to advanced capabilities. The Markovian RSA technique, if novel and effective, could contribute to efficient test-time scaling with limited context carry-over.

major comments (4)
  1. [Abstract] Abstract: The headline performance numbers (e.g., 91.9% AIME'25, 89.6% HMMT'25) are stated without error bars, number of runs, exact prompting format, temperature, or whether multiple attempts were permitted, preventing assessment of statistical reliability and direct comparison to baselines such as DeepSeek-R1-0528.
  2. [Training Pipeline] Training Pipeline section: The four-stage RL cascade is outlined at a high level, yet no ablation tables or incremental performance deltas are supplied to isolate the contribution of each stage (reasoning warmup, 400-task RLVE-Gym, math/code RL, behavioral RL), leaving the attribution of gains to the described methods unsupported.
  3. [Markovian RSA] Markovian RSA section: The recursive aggregation procedure is introduced without pseudocode, formal complexity bounds, or head-to-head comparisons against standard test-time methods (self-consistency, beam search) on token budget versus accuracy, making it impossible to evaluate whether the 4K-token tail truly delivers the claimed efficiency.
  4. [Evaluation] Evaluation section: No decontamination logs, overlap statistics, or reference to checks between the synthetic code environments (drawn from competitive-programming sources) and the AIME/HMMT test sets are provided, which is load-bearing for the generalization claim given the high reported scores.
minor comments (3)
  1. [Abstract] Abstract: The phrase 'MoE++ architecture' is used without a brief definition or citation to prior Zyphra work, which may hinder readers new to the base model.
  2. [References] References: Ensure all mentioned models (DeepSeek-R1-0528, Gemini-2.5 Pro, etc.) are accompanied by full citations rather than names alone.
  3. [Training Pipeline] Notation: The free parameters listed in the training description (expert routing, RL reward weights) should be tabulated with their chosen values for reproducibility.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for their thorough and constructive review of the ZAYA1-8B technical report. The comments identify key areas where additional details would improve reproducibility and evaluation of the claims. We address each major comment below and have revised the manuscript to incorporate the suggested improvements where feasible.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline performance numbers (e.g., 91.9% AIME'25, 89.6% HMMT'25) are stated without error bars, number of runs, exact prompting format, temperature, or whether multiple attempts were permitted, preventing assessment of statistical reliability and direct comparison to baselines such as DeepSeek-R1-0528.

    Authors: We agree that these details are required for proper assessment. The revised manuscript updates the abstract and Evaluation section with error bars from five independent runs, the exact prompting template, temperature=0.6, and explicit statement that Markovian RSA uses only the recursive aggregation without additional independent sampling attempts. A matched-conditions comparison table versus DeepSeek-R1-0528 has also been added. revision: yes

  2. Referee: [Training Pipeline] Training Pipeline section: The four-stage RL cascade is outlined at a high level, yet no ablation tables or incremental performance deltas are supplied to isolate the contribution of each stage (reasoning warmup, 400-task RLVE-Gym, math/code RL, behavioral RL), leaving the attribution of gains to the described methods unsupported.

    Authors: We acknowledge the value of ablations for attribution. Full retraining ablations are infeasible due to compute cost, but the revision adds an appendix with incremental checkpoint results on core benchmarks after the RLVE-Gym and math/code RL stages. This supplies partial deltas while noting the sequential dependencies in the cascade. revision: partial

  3. Referee: [Markovian RSA] Markovian RSA section: The recursive aggregation procedure is introduced without pseudocode, formal complexity bounds, or head-to-head comparisons against standard test-time methods (self-consistency, beam search) on token budget versus accuracy, making it impossible to evaluate whether the 4K-token tail truly delivers the claimed efficiency.

    Authors: We have added pseudocode for the Markovian RSA procedure. The revision includes formal complexity analysis (linear time in rounds with constant 4K-token memory) and direct experimental comparisons on AIME'25 against self-consistency and beam search, confirming improved accuracy per token budget under the bounded-tail constraint. revision: yes

  4. Referee: [Evaluation] Evaluation section: No decontamination logs, overlap statistics, or reference to checks between the synthetic code environments (drawn from competitive-programming sources) and the AIME/HMMT test sets are provided, which is load-bearing for the generalization claim given the high reported scores.

    Authors: The revised Evaluation section now contains a decontamination subsection with n-gram overlap statistics (<0.05% overlap) and explicit verification that the synthetic code environments were generated from sources disjoint from AIME/HMMT problems via ID and semantic checks. Full logs are included in the supplementary material. revision: yes

Circularity Check

0 steps flagged

No significant circularity; all claims are direct empirical benchmark reports

full rationale

The paper reports measured performance of ZAYA1-8B on external benchmarks (AIME'25, HMMT'25, math/coding tasks) after describing a training pipeline (pretraining with answer-preserving trimming, four-stage RL cascade, Markovian RSA test-time aggregation). No equations, fitted parameters, or first-principles derivations are presented whose outputs reduce to the inputs by construction. No self-citation load-bearing uniqueness theorems, ansatzes smuggled via citation, or renaming of known results occur. Benchmark scores are falsifiable external measurements, not self-referential predictions. This is the normal case for an empirical technical report.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 1 invented entities

The central performance claims rest on the effectiveness of the described training pipeline and Markovian RSA; these depend on standard machine-learning assumptions plus the unverified premise that benchmark scores measure the intended capabilities.

free parameters (2)
  • MoE expert count and routing hyperparameters
    Chosen to achieve 700M active parameters out of 8B total; specific values not stated in abstract.
  • RL stage hyperparameters and reward weights
    The four-stage cascade and 400-task curriculum require numerous tuned values whose selection is not detailed.
axioms (2)
  • domain assumption Benchmark problems in AIME'25 and HMMT'25 are uncontaminated and measure genuine reasoning ability
    Invoked when claiming the model reaches 91.9% and 89.6% respectively.
  • domain assumption The answer-preserving trimming scheme and synthetic code environments produce high-quality reasoning data
    Used to justify including reasoning data from pretraining onward.
invented entities (1)
  • Markovian RSA no independent evidence
    purpose: Recursive aggregation of parallel reasoning traces that retains only a bounded-length tail
    Newly introduced method whose independent evidence is limited to the reported benchmark gains.

pith-pipeline@v0.9.0 · 5654 in / 1566 out tokens · 49634 ms · 2026-05-08T17:30:38.622424+00:00 · methodology

discussion (0)

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Reference graph

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