Minbyul

Qwen3.5-35B-A3B-Correct

README

License: apache-2.0

Model description

Architecture: Qwen3.5-35B-A3B — a mixture-of-experts (MoE) decoder-only transformer with ~35B total parameters and ~3B active per token. Architecture, tokenizer, and chat/tool-calling template are unchanged from the base model.
Training context length: 131,072 tokens (full agent trajectories, multi-turn with interleaved tool calls and tool results).
Behavior domain: multi-turn web-browsing agentic tool use (search / page-open / in-page find style tool surface) with explicit reasoning.

Training procedure

Full-parameter supervised fine-tuning on a Megatron-based distributed training stack. Identical recipe to the sibling arms; only the data intervention differs.

Table with columns: Hyperparameter, Value
Hyperparameter	Value
Epochs	2
Global batch size	128
Learning rate	5e-6, cosine decay to 5e-7
Sequence length	131,072
Objective	token-level cross-entropy on assistant turns (trajectory imitation)

Intervention: correctness filtering (outcome-conditioned selection)

Each trajectory's final answer is checked against its reference answer with a deterministic matching rule (no model-based judging), and only trajectories that pass are kept. This is the classic rejection-sampling-style selection baseline of the study: it conditions on outcome only, and is agnostic to how the agent behaved along the way. In particular, a correct-but-wasteful trajectory (one that reaches the right answer and then keeps searching) survives this filter — which is exactly the contrast the four-arm design probes against the process-conditioned Drop and Repair arms and their A–G over-reflection taxonomy (post-answer verification loops, redundant re-search after sufficient evidence, ungrounded reasoning).

Table with columns: Arm, Intervention class
Arm	Intervention class
Asis	none — unmodified trajectory imitation baseline
Drop	typed whole-trajectory deletion of pathological over-reflection types
Repair	taxonomy-guided surgical repair (truncate post-evidence over-search tails; remove ungrounded trajectories)
Correct (this model)	correctness-filtered subset (deterministic answer matching)

An RL continuation of this arm is released as Qwen3.5-35B-A3B-Correct-GRPO.

Intended use

Research artifact. Intended for studying outcome-conditioned data selection as a baseline against process-conditioned interventions for over-reflection, stopping behavior, and search efficiency in tool-using web agents, and as an RL initialization. It expects a browsing-agent tool surface compatible with its training format (function-calling tools for web search, page opening, and in-page lookup).

Limitations

Behavior is tuned for the browsing-agent tool surface described above; it is not a general-purpose assistant fine-tune.
Outcome filtering keeps correct-but-inefficient demonstrations, so over-reflection pathologies are not removed by construction.
Correctness filtering shrinks the training set relative to the Asis baseline; arm-to-arm differences confound data quality with quantity by construction (part of what the study measures).
No additional safety alignment was applied beyond what the base model provides.
Trained primarily for English-language reasoning traces.

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Model Details

Model Provider

Minbyul

Model Tree

Base

Qwen/Qwen3.5-35B-A3B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality