Manas-64M · Agentic RL
The SFT model trained with reinforcement learning on multi-turn tool use: up to 3 turns of <tool_call> → tool result → answer, rewarded when the final answer matches a verifiable ground truth.
Try it: chat Space · Every stage: collection · Code: github.com/kuluruvineeth/manas · Data: manas_dataset
Results
| first 3 log points | last 3 log points | |
|---|---|---|
| mean reward | -0.67 | 0.21 |
| KL to the SFT reference | 0.022 | 0.053 |
| tool calls per trajectory | 2.54 | 1.54 |
| unfinished trajectories | 75% | 0% |
800 steps in 87 minutes, one log point every 50 steps. Each point is a small batch, so read the trend, not single values.
Benchmarks
Zero-shot multiple choice, 500 items per task, scored by comparing answer log-likelihoods (scripts/evaluate.py).
| benchmark | accuracy | random chance | items |
|---|---|---|---|
| arc_challenge | 21.0% | 25.1% | 500 |
| arc_easy | 32.4% | 25.0% | 500 |
| hellaswag | 35.0% | 25.0% | 500 |
| mmlu | 26.0% | 25.0% | 500 |
| openbookqa | 25.4% | 25.0% | 500 |
| average | 28.0% |
A 64M model has far less capacity than these benchmarks assume, so scores sit near random chance. They are published because hiding them would be dishonest, and they move by about a point between stages, which is within noise at 500 items.
Use
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "kuluruvineeth/manas-64m-agent"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)
messages = [{"role": "user", "content": "What is the capital of France?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True)
out = model.generate(**inputs, max_new_tokens=60, do_sample=False)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
The export is a standard transformers checkpoint, so vLLM and SGLang load it with no custom code (vllm serve kuluruvineeth/manas-64m-agent).
Training
- Lineage: starts from
manas-64m-full-sft. - Method: GRPO loss over 4 rollouts per task, β = 0.1 KL to the SFT reference, up to 256 new tokens per turn.
- Data:
agent_rl.jsonl: tasks from 6 deterministic generators over mock tool tables, so every ground truth is correct by construction (sources and licenses). - Trainer:
trainer/train_agent.py, peak learning rate 3e-7 (from the training log). - Hardware: one A100 or H100 GPU on Modal (
gpu/modal_train.py). - Architecture: Qwen3 layout: 768 hidden, 8 layers, 8 query / 4 key-value heads (head dim 96), SwiGLU MLP of width 2,432, RoPE θ = 10⁶, tied embeddings, 6,400-token byte-level BPE vocabulary. 63,912,192 parameters.
Limitations
English only. At this size the model has little world knowledge: it is confidently wrong about facts that need it, loses the thread in long conversations, and should not be used for anything that matters. It is a teaching and research artifact for studying how each training stage changes a model.
Files
model.safetensors,config.json,tokenizer.json,chat_template.jinja: the transformers exportagent_768.pth: the raw training checkpoint for the training codeagent_768_metrics.jsonl,agent_768_curves.png: the training log and its plot