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burtenshaw  published a Space 6 days ago
openenv/mini-swe-async-grpo-trackio-20260529
burtenshaw  updated a Space 6 days ago
openenv/mini-swe-async-grpo-trackio-20260529
burtenshaw  updated a Space 23 days ago
openenv/terminus_env
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sergiopaniego 
posted an update 1 day ago
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91
The recording from our talk: "From Responses To Trajectories: Multi-Turn and Multi-Environment RL" from PyTorch Conf Europe is live!

@kashif and I covered the latest advances in multi-turn GRPO in TRL: trajectories, tool use, envs, and agentic post-training at scale

https://www.youtube.com/watch?v=rPBeXFntJSU
sergiopaniego 
posted an update 1 day ago
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78
how do you sync a trillion parameter model every RL step without a shared cluster? we just wrote a blog about it, led by @aminediroHF

what I like the most is the way it proves you can use the Hub for basically everything 🧐 → trainer on one machine, vLLM in a HF Space, the wordle env in another HF Space and weights going through a Hub Bucket. no shared cluster, just HTTPS

it works because ~99% of bf16 weights don't change between RL steps so you only sync the diff. 1.2 GB to 25 MB of payload per step

https://huggingface.co/blog/delta-weight-sync
sergiopaniego 
posted an update 2 days ago
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2233
most multi-turn RL loops have a silent bug: you decode the model's output to detect tool calls, then re-tokenize the conversation for the next turn. BPE isn't invertible, so decode then re-encode can land on different ids. gradient ends up on tokens the model never sampled. no crash, just quietly wrong math and broken training

@qgallouedec wrote a super educational blog on MITO (message-in, token-out) vs TITO (token-in, token-out) and how you might fix the problem above

go read it 🤓

https://qgallouedec-tito.hf.space/
sergiopaniego 
posted an update 3 days ago
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6167
new banger blog alert 🚨

@ariG23498 is starting a blog series about profiling in pytorch and part 1 just dropped

takes you from the simplest scenario to actually knowing what your gpu is doing. if you have never opened a profiler trace this is where you start

covers torch.profiler from scratch. reading tables and traces, overhead bound vs compute bound, the full dispatch chain from python to gpu kernels, and what torch.compile is actually fusing under the hood

find it here: https://huggingface.co/blog/torch-profiler
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sergiopaniego 
posted an update 6 days ago
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152
If you have a github repo, you basically have an RL training environment

We're introducing Repo2RLEnv (built by @AdithyaSK ), a tool that mines PRs, commits, CVEs and turns them into verifiable sandboxed tasks with real reward signals, automatically

Outputs to Harbor spec so you can plug it straight into RL training or coding-agent eval

> repo: https://github.com/huggingface/Repo2RLEnv
> collection with envs: https://huggingface.co/collections/AdithyaSK/repo2rlenv-verifiable-rl-environments
burtenshaw 
published a Space 6 days ago
burtenshaw 
updated a Space 6 days ago
sergiopaniego 
posted an update 7 days ago
sergiopaniego 
posted an update 10 days ago
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9941
Harness, Scaffold, Context Engineering, Agent... do you actually know what they mean?

We wrote an AI agent glossary and tried to make sense of it all with simple definitions and real examples

↓ go read it ↓

https://huggingface.co/blog/agent-glossary
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burtenshaw 
updated a Space 23 days ago
burtenshaw 
published a Space 23 days ago
qgallouedec 
posted an update 25 days ago
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10223
Shipped hf-sandbox! 🥡

🧪 Running an eval that executes model-generated C on a few thousand prompts? You probably don't want any of that on your laptop.
Just shipped hf-sandbox, a Modal-style sandbox API on top of Hugging Face Jobs. Spin up an isolated, ephemeral container, run untrusted code, get the result back. No Docker on your laptop, no infra to manage.

Just pip install hf-sandbox.

Early days (v0.1); feedback and issues very welcome:
👉 https://github.com/huggingface/hf-sandbox
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qgallouedec 
posted an update 27 days ago
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370
**TRL v1.4 is out 🚀** Chunked NLL loss for SFT and a first-class **OpenReward** integration.

**Chunked NLL loss for SFT — drops peak VRAM by up to 14×**

Standard SFT materializes a full [batch × seq × vocab] logits tensor before computing cross-entropy, which dominates peak memory at long context lengths. The new loss_type="chunked_nll" path drops ignored-label tokens before the lm_head matmul and computes cross-entropy in checkpointed chunks of 256.

Peak GPU memory, AdamW fp32:
- Qwen3-14B, 8×H100 FSDP2, 16k seq: 58.9 GB → 38.9 GB
- Qwen3-4B, 1×H100 80GB, 16k seq: OOM → 63.8 GB
- Qwen3-32B, 8×H100 FSDP2, 8k seq: OOM → 71.2 GB

End-to-end it's consistently as fast or faster than nll, and unlocks sequence lengths that don't fit at all under the standard path.

SFTConfig(loss_type="chunked_nll")


Works with PEFT and VLMs out of the box.

**Open Reward Standard environment adapter**

The new trl.experimental.openreward adapter plugs any environment speaking the [Open Reward Standard](https://openrewardstandard.io) protocol into any TRL trainer that takes an environment_factory. One string — a catalog name or a URL — wires the dataset, factory, and reward_func slots; tools are bound dynamically from JSON Schema, no per-env wrapper code:

from trl import GRPOTrainer
from trl.experimental.openreward import OpenRewardSpec

spec = OpenRewardSpec("Eigent/SETA", num_tasks=64)

trainer = GRPOTrainer(
    ...,
    train_dataset=spec.train_dataset,
    environment_factory=spec.environment_factory,
    reward_funcs=spec.reward_funcs,
)


v1.4 also brings MFU helpers for dense + MoE models, GRPO support for Liger 0.8.0 (delta clipping + VESPO + KL bias correction), Tülu 3's length-normalized DPO loss, four more training chat templates (Cohere, Cohere2, Gemma 3, Qwen3-2507), and a 5+ GB CUDA memory leak fix in activation offloading.

Full release notes: https://github.com/huggingface/trl/releases/tag/v1.4.0
sergiopaniego 
posted an update 27 days ago
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1882
OpenEnv is growing fast in tutorials. If you're looking to get started with RL environments, check them out

> evaluate your agents using OpenEnv
> learn how rewards work via rubrics
> connect agents via MCP
> many moreeeee!

anything you think it's missing?

https://meta-pytorch.org/OpenEnv/tutorials/index.html
sergiopaniego 
posted an update 28 days ago
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870
OpenEnv already ships 🚢 with a ready-to-deploy RLM environment on free HF Spaces

Drop "Attention Is All You Need", write code that spawns parallel LLM calls → ✅ correct answer, reward 1.0, in 4.2s

Run GRPO (TRL) → model learns to write that search strategy itself

test it yourself → sergiopaniego/repl-env
check out OpenEnv → https://github.com/meta-pytorch/OpenEnv
qgallouedec 
posted an update about 1 month ago
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8083

TRL v1.3 ships day-one training support for Qwen 3.6 🚀

The new Qwen 3.6 family (Qwen/Qwen3.6-27B, Qwen/Qwen3.6-35B-A3B) reuses the Qwen3.5-MoE architecture but ships a slightly different chat template, so we updated the stack end-to-end: new training template with {% generation %} markers, tool-call response schema routing, tiny test models for the VLM matrix.

SFT with assistant-only loss works out of the box:

from trl import SFTConfig, SFTTrainer

trainer = SFTTrainer(
    model="Qwen/Qwen3.6-27B",
    args=SFTConfig(assistant_only_loss=True),
    train_dataset=dataset,
)
trainer.train()


So does GRPO tool-calling — just hand tools=[...] to GRPOTrainer.

v1.3 also brings a new experimental TPO trainer (Triple Preference Optimization), speculative decoding in trl vllm-serve (Qwen3 MTP / Eagle3 drafts), 12 more KTO ↔ DPO alignment PRs (KTO promotion to stable is now in reach), three more {% generation %} chat templates (Gemma/Gemma 2, Phi-3, GLM-4-MoE), and a chunky SFT entropy bug fix.

Full release notes: https://github.com/huggingface/trl/releases/tag/v1.3.0
qgallouedec 
posted an update about 2 months ago
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2031
TRL v1.2 introduces the SSDTrainer 🚀

Simple Self-Distillation (SSD) from Apple's paper "Embarrassingly Simple Self-Distillation Improves Code Generation" is now available as an experimental trainer in TRL.

The recipe is as minimal as the name suggests: sample completions from the model itself at a training-time temperature, then fine-tune on those raw, unverified samples with plain cross-entropy. No reward model. No verifier. No teacher model. No reinforcement learning. Just prompts and the model.

from trl.experimental.ssd import SSDConfig, SSDTrainer

trainer = SSDTrainer(
    model="Qwen/Qwen3-4B-Instruct",
    args=SSDConfig(temperature=0.6, top_k=20, top_p=0.95),
    train_dataset=dataset,
)
trainer.train()


v1.2 also ships expanded tool-calling support (LLaMA 3.1 / 3.2, DeepSeek-V3), another round of KTO ↔ DPO alignment getting us closer to promoting KTO to stable, a big GRPO simplification for overlong tool results, deprecation of use_transformers_paged, and key fixes for VLM response parsing.

Full release notes: https://github.com/huggingface/trl/releases/tag/v1.2.0
sergiopaniego 
posted an update about 2 months ago
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1422
Earlier this month, Apple introduced Simple Self-Distillation: a fine-tuning method that improves models on coding tasks just by sampling from the model and training on its own outputs with plain cross-entropy

And… it's already supported in TRL, built by Kashif Rasul. you can really feel the pace of development in the team 🐎

Paper by Ruixiang ZHANG, He Bai, Huangjie Zheng, Navdeep Jaitly, Ronan Collobert, Yizhe Zhang at Apple 🍎

How it works: the model generates completions at a training-time temperature (T_train) with top_k/top_p truncation, then fine-tunes on them with plain cross-entropy. no labels or verifier needed

You can try it right away with this ready-to-run example (Qwen3-4B on rStar-Coder):
https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd.py
or benchmark a checkpoint with the eval script:
https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd_eval.py

One neat insight from the paper: T_train and T_eval compose into an effective T_eff = T_train × T_eval, so a broad band of configs works well. even very noisy samples still help

Want to dig deeper?

Paper: Embarrassingly Simple Self-Distillation Improves Code Generation (2604.01193)
Trainer docs: https://huggingface.co/docs/trl/main/en/ssd_trainer
sergiopaniego 
posted an update about 2 months ago
sergiopaniego 
posted an update 2 months ago