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bmonikraj/qwen3-14b-search-plan-act
qwen3-14b-search-plan-act is a reinforcement learning model from bmonikraj. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Full-weight checkpoint (LoRA merged) of Qwen/Qwen3-14B, trained with GRPO (via TRL's multi-turn environmentfactory tool-calling API) on searchplanact: a procedurally-generated, closed-book multi-turn task requiring th…
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From the Hugging Face model README
search_plan_actFull-weight checkpoint (LoRA merged) of Qwen/Qwen3-14B, trained with GRPO
(via TRL's multi-turn environment_factory tool-calling API) on
search_plan_act: a procedurally-generated, closed-book multi-turn task
requiring the model to search for facts, read_record/update_record/
link_records on retrieved entities, and finish, looping through tool
calls until the goal is achieved or it gives up. Reward decomposes into
outcome / grounding / stop-behavior components, with an explicit
adversarial-pilot check that no reward-hacking strategy (reckless guessing,
spamming searches, never finishing) scores above genuine goal-directed play.
Training: LoRA (r=32, alpha=64) on all attention+MLP projections, GRPO via TRL 1.9.2 + vLLM colocate generation, 250 steps, effective batch size 32, 4x GH200 GPUs. Memory-retuned specifically for this model size (halved per-device batch, doubled gradient accumulation to preserve the same effective batch, and a modest vLLM KV-cache allocation bump) -- a 14B model needs roughly two full weight copies coexisting on one GPU under colocate mode (one for training, one for vLLM's inference engine), which the smaller-model default settings didn't budget for.
Evaluated pre-RL (base Qwen/Qwen3-14B) vs post-RL (this checkpoint) on
held-out real-world and synthetic benchmarks, all using vLLM-accelerated
generation with the model's own chat template (apples-to-apples generation
path in both rows):
| Benchmark | Pre-RL | Post-RL (this checkpoint) |
|---|---|---|
| MuSiQue (EM / F1, n=2417) | 0.342 / 0.466 | 0.343 / 0.469 |
| 2WikiMultihopQA (EM / F1, n=12576) | 0.578 / 0.676 | 0.578 / 0.676 |
| search_plan_ood (mean reward, n=100) | 0.030 | 0.040 |
| BALROG BabyAI (mean episode return, n=50) | 0.000 | 0.000 |
Honest finding: this 14B checkpoint shows only a small gain on
search_plan_ood and is flat everywhere else, including BALROG (where
smaller Qwen3 sizes showed a small real gain). Compare against the sibling
bmonikraj/qwen3-8b-search-plan-act checkpoint, which showed much stronger,
more consistent transfer under the identical training recipe -- RL transfer
here is genuinely model-specific, not simply a function of scale.
model.safetensors, config.json, tokenizer.json, tokenizer_config.json,
generation_config.json, chat_template.jinja -- the full merged checkpoint.adapter/ -- the raw LoRA adapter (pre-merge), kept for reference.