Dev tools · alternatives · 2026

24 alternatives to Shrinking Fractional Dimensions with Reinforcement Learning

Code for CORL 2020 paper: Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement Learning. - sgillen/fractal_rl

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Shrinking Fractional Dimensions with Reinforcement Learning launched in 2020; newer entries below may have overtaken it.

1
Boldly go where Gradient Descent has never gone before with DiscoGrad▲232
Trying to do gradient descent using automatic differentiation over branchy programs? Or to combine them with neural networks for end-to-end training? Then this might be interesting to you. We develped DiscoGrad, a tool for automatic differentiation through C++ programs involving input-dependent control flow (e.g., "if (f(x) < c) { ... }", differentiating wrt. x) and randomness. Our initial motivation was to enable the use of gradient descent with simulations, which often rely heavily on such discrete branching. The latter makes plain autodiff mostly useless, since it can only account for the…
2024 · github.com · its alternatives →
2
Fast Deep Reinforcement Learning Course▲148
I worked on this applied Deep Reinforcement Learning course for the better part of 2021. I made a Datacamp course [0] before, and this served as my inspiration to make an applied Deep RL series. Normally, Deep RL courses teach a lot of mathematically involved theory. You get the practical applications near the end (if at all). I have tried to turn that on its head. In the top-down approach, you learn practical skills first, then go deeper later. This is much more fun. This course (the first in a planned multi-part series) shows how to use the Deep Reinforcement Learning framework RLlib to…
2022 · courses.dibya.online · its alternatives →
3
LeanRL: Fast PyTorch RL with Torch.compile and CUDA Graphs▲53
We're excited to announce that we've open-sourced LeanRL, a lightweight PyTorch reinforcement learning library that provides recipes for fast RL training using torch.compile and CUDA graphs. By leveraging these tools, we've achieved significant speed-ups compared to the original CleanRL implementations - up to 6x faster! Reinforcement learning is notoriously CPU-bound due to the high frequency of small CPU operations. PyTorch's powerful compiler can help alleviate these issues, but comes with its own costs. LeanRL addresses this challenge by providing simple recipes to accelerate your…
2024 · github.com · its alternatives →
4
I RL-trained an agent that trains models with RL (for ~$1.3k)▲107
RL-training an AI agent to RL-train AI agents. Contribute to Danau5tin/ai-trains-ai development by creating an account on GitHub.
Jul 2026 · github.com · its alternatives →
5
A radically more scalable alternative to Bayesian Optimization▲96
Experimental Global Optimization Algorithm. Contribute to chrisstroemel/Simple development by creating an account on GitHub.
2018 · github.com · its alternatives →
6AR
Animating randomized L-Systems in real time▲110
2020 · ehrenjn.github.io · its alternatives →
7
Terminal-Bench-RL: Training long-horizon terminal agents with RL▲125
After training calculator agent via RL, I really wanted to go bigger! So I built RL infrastructure for training long-horizon terminal/coding agents that scales from 2x A100s to 32x H100s (~$1M worth of compute!) Without any training, my 32B agent hit #19 on Terminal-Bench leaderboard, beating Stanford's Terminus-Qwen3-235B-A22! With training... well, too expensive, but I bet the results would be good! *What I did*: - Created a Claude Code-inspired agent (system msg + tools) - Built Docker-isolated GRPO training where each rollout gets its own container - Developed a multi-agent…
2025 · github.com · its alternatives →
8
Qwen3.6-35B-A3B▲124
The open sparse MoE model for agentic coding
Apr 2026 · qwen.ai · its alternatives →
9
Monostate AItraining▲59
Fine-tuning, RL, and inference in one CLI
Dec 2025 · github.com · its alternatives →
10RE
RULER – Easily apply RL to any agent▲81
Hey HN, Kyle here, one of the co-founders of OpenPipe. Reinforcement learning is one of the best techniques for making agents more reliable, and has been widely adopted by frontier labs. However, adoption in the outside community has been slow because it's so hard to implement. One of the biggest challenges when adapting RL to a new task is the need for a task-specific "reward function" (way of measuring success). This is often difficult to define, and requires either high-quality labeled data and/or significant domain expertise to generate. RULER is a drop-in reward function that works…
2025 · openpipe.ai · its alternatives →
11
Evolving fractals with genetic algorithms▲42
Random fractal or the secret behind my tree. Contribute to victorqribeiro/randomFractal development by creating an account on GitHub.
2020 · github.com · its alternatives →
12
Season's greetings from Attractor, a fractal graphing calculator▲6
An interactive preset in Attractor, a tool to generate and animate fractals based on iterated functions of complex numbers
2020 · attr.actor · its alternatives →
13
A weekend replication of STOKE, a stochastic superoptimiser▲58
Haskell re-implementation of STOKE, the stochastic superoptimizer - blaze/notebooks/tutorial.ipynb at master · bollu/blaze
2019 · github.com · its alternatives →
14
Explore tilings of the hyperbolic plane by moving the mouse▲46
Explore tilings on the hyperbolic plane. Contribute to timhutton/hyperplay development by creating an account on GitHub.
2014 · github.com · its alternatives →
15
RLSL – Reinforcement Learning for Skip Lists (DBs and AI)▲9
Reinforcement Learning for Skip Lists. Contribute to cioc/rlsl development by creating an account on GitHub.
2018 · github.com · its alternatives →
16
Hopalong Attractor. An old classic with a new perspective in 3D▲25
Generative Density Approximation for Deterministic Chaos The Hopalong Attractor - ratwolfzero/hopalong_python
Mar 2026 · github.com · its alternatives →
17
Next-Gen AI Training: LLM-RLHF-Tuning with PPO and DPO▲30
Comprehensive toolkit for Reinforcement Learning from Human Feedback (RLHF) training, featuring instruction fine-tuning, reward model training, and support for PPO and DPO algorithms with various configurations for the Transformer 5 models including Qwen 3.0 - raghavc/LLM-RLHF-Tuning-with-PPO-and-DP
2024 · github.com · its alternatives →
18AR
A real-time 4D fractal explorer in the browser using WebGPU▲26
Hi HN, I've always been interested in fractals, especially the Mandelbrot and Julia sets. A few years ago I created a 2d viewer of this inherently 4d space. But the other day I decided to ask Claude and GPT how to make this a full RT 3d explorer. A few hours later and this was vibe coded. To use it you can use the mouse to rotate the fractal the the mouse wheel to zoom in and out. to map from 4d to 3d, one of the dims is mapped to an adjustable slider. the there is also a clipping plane slider to help visualize the internal structures of the fractal. I have mixed feelings about vibe coding.…
Dec 2025 · bryanjj.github.io · its alternatives →
19
Supercharging RL with Hyper-Efficient Online Opt, +165% in 2h, $10▲32
Arc enables continual learning for agents to adapt, learn, and transfer knowledge in live environments. Open-source SDK for building agents that learn from experience and improve over time.
2025 · arc.computer · its alternatives →
20RA
RL Agent that can auto-optimize your LLM prompts▲14
Hey everyone! Along with my team, I've developed a reinforcement learning system that automatically optimizes LLM prompts, complete with a visualization feature to track both prompt structure and learning progress over time. Take a look here: https://nomadic-ml.github.io/nomadic/cookbooks/Nomadic_Promp... Check out our website too:https://www.nomadicml.com/ In terms of how this visualization works: The RL Prompt Optimizer employs a reinforcement learning framework to iteratively improve prompts used for language model evaluations. At each episode, the…
2024 · nomadic-ml.github.io · its alternatives →
21
Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)▲21
Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com · its alternatives →
22FF
Fractal Flyer – Explore Dynamic 3D Fractals in WebGL▲5
2015 · neverwork.in · its alternatives →
23
NanoRL – RL training for LLMs in ~1,800 lines▲11
The smallest async RL trainer I could write: one loop that runs REINFORCE on CartPole on a laptop and async GRPO on a cluster (e.g. 8xH100 trainer, 8 vLLM workers, ran as a [SkyPilot job group](https://docs.skypilot.ai/en/latest/examples/job-groups.html) on k8s ). All without Ray or TRL or DeepSpeed etc., workers talk to the trainer over stdlib HTTP.
Aug 2026 · github.com · its alternatives →
24
Reinforcement Learning from Scratch with TypeScript▲7
Reinforcement Learning (DQN) from scratch with zero dependencies applied to a simple car driving game - desi-ivanov/cars-rl
2023 · github.com · its alternatives →

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