Qwen3.5-4B-MatRL-MT-SFT

Qwen3.5-4B — MatRL multi-turn SFT (cold start)

Qwen/Qwen3.5-4B cold-started on amayuelas/matrl-sft-mt: 1,123 multi-turn tool-use episodes for crystal-structure inverse design.

This is the cold-start checkpoint. Its job is format and the tool loop — propose, evaluate, refine, and above all commit within the turn budget. The chemistry is learned afterwards by RL; see amayuelas/Qwen3.5-4B-MatRL-MT-RL.

Why a cold start is needed

The base model essentially never commits. Across 2,880 multi-turn rollouts it made 3 submit calls — it proposes prolifically (~5.6 candidates/rollout) and evaluates sparingly, then runs out of turns. Strict multi-turn SUN is therefore 0% by construction: a commitment failure, not a chemistry failure. Teaching the agent to close an episode is precisely what this stage installs.

Training

Trained on 4×A100-40GB. Final loss ~0.36.

Context parallelism uses ulysses, not ring: Qwen3.5 is a hybrid with linear attention (DeltaNet) layers, and ring attention is a softmax-attention algorithm that does not apply to them.

Important: thinking channel

This model is trained with reasoning in the native thinking channel (reasoning_content → <think>), preserved across tool calls. Do not evaluate it with enable_thinking=false — that disables exactly the behavior this run trains.

Serving

Qwen3.5 is a VL-capable model class, so vLLM requires an image-processor config even for text-only serving. preprocessor_config.json and video_preprocessor_config.json are included; without them vLLM fails on load.