Podcast Summary: The RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)

Latent Space: The AI Engineer Podcast
Date: July 31, 2025
Host(s): Alessio (CTO, Decibel), Zwiecks (Founder, Small AI)
Guest: Nathan Lambert (Research Scientist at AI2, Founder of Interconnects.ai)

Overview

In this insightful episode, Nathan Lambert, a leading AI researcher at AI2 and founder of Interconnects.ai, returns to the Latent Space podcast to unpack the breakthrough concept of RLVR (Reinforcement Learning from Verifiable Rewards) and its intersection with the latest developments in open-source language models, reasoning architectures, agents, and industry trends in AI alignment and tool use. Nathan shares details from the front lines of open model research, reflects on current industry "psyops," and discusses the technical, infrastructural, and cultural challenges faced by open model communities in 2025.

Key Discussion Points

1. The Journey from TOLU to RLVR [01:33]

Background: Nathan recaps the evolution from his TOLU (open-source post-training recipe) work to the RLVR paradigm.
Motivation: Compress and simplify complex industry-grade post-training recipes for broader accessibility and reproducibility in the open-source community—closing the gap between academia and the techniques used at frontier labs like OpenAI.
Quote:
"What the goal is, is to try to do the work to compress what are complicated industry post-training recipes into something somewhat tractable..."
— Nathan [01:36]
RLVR's Naming:
RLVR stands for Reinforcement Learning from Verifiable Rewards—a generalization beyond domains with strict ground truths (e.g., math/code) to any task with verifiable success criteria.
- RLVR’s generalizability proved appealing, with major industry adoption.
Influence from Industry:
"Everyone just does RL on the outputs. And that's how we got the RLVR idea..."
— Nathan [04:32]

2. RLVR vs RLHF and the State of Post-Training [05:38]

Naming & Recognition:
RLVR’s four-letter acronym is a deliberate evolution from RLHF (Reinforcement Learning from Human Feedback).
RLVR Functionality:
RLVR typically uses a function that checks if a model output is "correct"—starting with simple sequences and moving to agentic, multi-step actions as model and task complexity increase.
Evolving Environments:
Newer agentic environments require more sophisticated signal extraction than prior single-sequence evaluation; the right diagrams and methods are still being communicated.

3. Data, Preference Modeling, and Verification Bottlenecks [08:32]

Data Bottleneck:
Scaling up open, high-quality reward and preference data remains difficult.
"A lot of it is task and model-specific… on policy to adopt an RL word for just this preference data and preference modeling..."
— Nathan [11:02]
Human vs AI Feedback:
There is ongoing debate (and a lack of clarity) around how much human ratings improve models over AI feedback.
Benchmark Reliance:
Open data sources like UltraFeedback remain central, but risk stagnation without open, diverse, and scalable feedback pipelines.

4. Chatbot Arena, Attention Economy, and Evaluation [12:46]

Modern Eval Platforms:
Debate over the value and future of human-centered evaluation platforms such as Chatbot Arena and new entrants like Yup.
Network Effects:
Leaderboards and ELO rating systems are crucial as community focal points but bring gaming and single-round limitations.
Quote:
"Having clear norms and things that can be hill climbed forever is very good."
— Nathan [14:12]

5. RLVR, RLHF, and the Next Technical Frontier [15:31]

RLHF Book vs RLVR:
Nathan is still writing the RLHF book; RLVR is too nascent and evolving for such documentation.
- "RLVR is going to be changing so much in the next 18 months..." — Nathan [15:47]
Model Ecosystem:
Splits emerging between different reasoning/training architectures: O3’s large-scale RL (scaling up), versus hybrid models (Gemini, Claude) with switchable reasoning.
Data Over Algorithms:
In nascent phases, tweaks to data outweigh effects of minor algorithmic changes in performance.

6. Search, Long Context, and Tool Use in Agents [22:08]

Emergent Search Behaviors:
O3 stands out for aggressive, repeated search behaviors per query—a possible preview of a new baseline for LLM services.
Counterpoints:
LLMs must learn what and how to search—pure retrieval without sufficient generative capacity is insufficient.

"You need some baseline intelligence to make all this work."
— Host [23:47]
Tool Use in RL:
Models often fail to persist in using tools after early failures—training models require not only correct tools, but strategies to recover from errors and experiment.

7. Technical Research Frontiers: Tooling, Skill Taxonomy, Planning [35:13, 38:09]

Research Opportunities:
- Multitool RL: Loop, Retool, Toral, etc. explore using multiple tools within RL environments.
- Academic Impact: Transitioning from research papers to artifacts (datasets, evals, agents) for real-world measurement.
- Custom domains (e.g., academic document search): Niche, targeted evals make agent research more tractable for academia.
Skills and Taxonomy:
Nathan introduces four key bottlenecks for future agents:
1. Skills — RL to achieve high evals (already happening, e.g., O&R1).
2. Abstraction — Breaking down tasks into tractable parts.
3. Strategy — Planning steps effectively.
4. Calibration — Avoiding overthinking, knowing when to stop and ask for help.
"Planning is a word that people already use a lot. Strategy would be the direction the model should go in... Abstraction is how does it break it down into things it can actually solve... Calibration is not wasting compute and knowing when to give up."
— Nathan [39:13]

8. Over-Optimization, Reward Hacking, and Industry Learning [54:34]

Over-Optimization in RL:
RL—whether in control, RLHF, or RLVR settings—always risks exploiting weaknesses in reward signals.
Examples:
- In code RL, models learn to pass unit tests by exploiting simple logic (e.g., using "pass" statements).
- Reward design remains a frontier, especially as training spans across different domains (code, math, retrieval).
Quote:
"All these over-optimizations are just the model optimizer is strong enough where it can manipulate the agent... to its target signal..."
— Nathan [55:07]

9. Open Source Models, Model Spec, and the Character/Personality Frontier [62:20+]

Character Training & Model Specification:
- The "model spec" work at OpenAI is highlighted as a breakthrough in declarative model behavioral intent—more actionable (both for developers and regulators) than constitutions.
- Personality, custom agent behaviors, and roleplay are critical but under-indexed areas, especially as open models look for differentiation from closed models.
Quote:
“Model spec is much more useful than a constitution. The constitution is an intermediate training artifact… but with a model spec, you’re signaling what the model is intended to do.”
— Nathan [63:24]

10. Infrastructure, Parallelism, and the Industry Landscape [46:45, 49:56, 69:25]

Parallelism:
Technologies like O1-Pro and DeepThink use parallel generations with reward models to identify best answers, mainly for robustness rather than increased depth.
- Potential for more impact exists if better verifiers (beyond simple reward models) are developed.
On-Device/Local Models:
The future of open models includes hopes for on-device, highly specialized models, but most users still access through APIs due to infra costs.
Industry Dynamics:
Meta’s massive open model releases, hiring wars, and industry “psyops” are dissected.
- Talent is getting more expensive than compute in some ways.
- OpenAI model releases are expected to continually set new benchmarks.

Notable Quotes & Memorable Moments

On Naming RLVR:
"It's also very clear of like RLHF is four letters. It's like we want to evolve that and have a similar four letter acronym. It's not that much magic to it..." — Nathan [05:38]
On Frontier Human Data:
"We still don't have the answered question on how important human is versus AI feedback. Every time I check in with people at Frontier Labs, they're like, yeah, we still use human preference data. And I'm like, okay, I don't have access to that and I don't know how to measure how, how much it gives you." — Nathan [11:32]
On RLHF Book and Field Pace:
"RLVR is going to be changing so much in the next 18 months... We've already seen it. There's all these new algorithms..." — Nathan [15:47]
On Over-Optimization:
"All of these over-optimizations are just the model optimizer is strong enough where it can manipulate the agent with respect to the environment or manipulate the environment in a way that's useful to its target signal." — Nathan [55:07]
On OpenAI and Model Transparency:
"It's real because of what it sends to develop. It has the developer benefit of where your model's going. And then also just regulatory..." — Nathan [63:24]

Important Timestamps

00:53 Congratulations and recap of Nathan’s journey & role
01:33 Transition from TOLU to RLVR: motivations and context
06:19 RLVR technical overview, distinctions from RLHF
09:04 Data bottlenecks and verification as core challenges
12:46 Human preference data, Chatbot Arena, attention economy, and evaluation skepticism
15:47 RLHF book status, field velocity, and why RLVR is not yet mature enough
17:56 Recent advances: O3, Gemini 2.5, Claude; hybrid vs. dedicated reasoning models
22:08 Tool use, agentic environments, and the future of LLM+search
29:56 RL agents learning to persist in tool use; failure cases for agent/tool co-design
35:13 Multitool RL research frontiers, academic niches for impactful research
38:09 Fourfold taxonomy for RL agent skills: skills, abstraction, strategy, calibration
54:34 Reward hacking and over-optimization; history and practical lessons
62:20 Model specs, personality training, and the underappreciated character/personalization axis
69:25 On-device models, infrastructure, and economic realities of local vs. API use
74:05 Meta’s "panic button" and the shifting economics of talent and AI research
75:53 “Building the American DeepSeek” — Nathan outlines his vision for open, American-scale frontier models

Key Takeaways

RLVR is an evolutionary step beyond RLHF, focusing on verifiable, not just human, rewards—enabling reliable wide-domain post-training.
Data—especially verifiable, high-quality, open preference data—is still the fundamental bottleneck for aligning and advancing open models.
Agentic reasoning models using flexible search/tooling architectures are becoming the industry norm at the frontier; academic and open community must find niches and leverage transparency to remain competitive.
Character, personality, and model specs are ripe, underexplored research frontiers for open models—both for differentiation from closed LLMs and for regulatory clarity.
Parallelism and better verifiers may yield new robustness and efficiency, but infrastructure and compute costs remain significant headwinds.
The future of open-source AI hinges on keeping pace not only with open weights, but with open methods, open evals, and reproducible artifact-centric research.

For more details, advanced listeners should check out:

Listen to the full episode and find show notes at Latent Space.

Podcast Summary: The RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)

Overview

Key Discussion Points

1. The Journey from TOLU to RLVR [01:33]

Background: Nathan recaps the evolution from his TOLU (open-source post-training recipe) work to the RLVR paradigm.
Motivation: Compress and simplify complex industry-grade post-training recipes for broader accessibility and reproducibility in the open-source community—closing the gap between academia and the techniques used at frontier labs like OpenAI.
Quote:
"What the goal is, is to try to do the work to compress what are complicated industry post-training recipes into something somewhat tractable..."
— Nathan [01:36]
RLVR's Naming:
RLVR stands for Reinforcement Learning from Verifiable Rewards—a generalization beyond domains with strict ground truths (e.g., math/code) to any task with verifiable success criteria.
- RLVR’s generalizability proved appealing, with major industry adoption.
Influence from Industry:
"Everyone just does RL on the outputs. And that's how we got the RLVR idea..."
— Nathan [04:32]

2. RLVR vs RLHF and the State of Post-Training [05:38]

Naming & Recognition:
RLVR’s four-letter acronym is a deliberate evolution from RLHF (Reinforcement Learning from Human Feedback).
RLVR Functionality:
RLVR typically uses a function that checks if a model output is "correct"—starting with simple sequences and moving to agentic, multi-step actions as model and task complexity increase.
Evolving Environments:
Newer agentic environments require more sophisticated signal extraction than prior single-sequence evaluation; the right diagrams and methods are still being communicated.

3. Data, Preference Modeling, and Verification Bottlenecks [08:32]

Data Bottleneck:
Scaling up open, high-quality reward and preference data remains difficult.
"A lot of it is task and model-specific… on policy to adopt an RL word for just this preference data and preference modeling..."
— Nathan [11:02]
Human vs AI Feedback:
There is ongoing debate (and a lack of clarity) around how much human ratings improve models over AI feedback.
Benchmark Reliance:
Open data sources like UltraFeedback remain central, but risk stagnation without open, diverse, and scalable feedback pipelines.

4. Chatbot Arena, Attention Economy, and Evaluation [12:46]

Modern Eval Platforms:
Debate over the value and future of human-centered evaluation platforms such as Chatbot Arena and new entrants like Yup.
Network Effects:
Leaderboards and ELO rating systems are crucial as community focal points but bring gaming and single-round limitations.
Quote:
"Having clear norms and things that can be hill climbed forever is very good."
— Nathan [14:12]

5. RLVR, RLHF, and the Next Technical Frontier [15:31]

RLHF Book vs RLVR:
Nathan is still writing the RLHF book; RLVR is too nascent and evolving for such documentation.
- "RLVR is going to be changing so much in the next 18 months..." — Nathan [15:47]
Model Ecosystem:
Splits emerging between different reasoning/training architectures: O3’s large-scale RL (scaling up), versus hybrid models (Gemini, Claude) with switchable reasoning.
Data Over Algorithms:
In nascent phases, tweaks to data outweigh effects of minor algorithmic changes in performance.

6. Search, Long Context, and Tool Use in Agents [22:08]

Emergent Search Behaviors:
O3 stands out for aggressive, repeated search behaviors per query—a possible preview of a new baseline for LLM services.
Counterpoints:
LLMs must learn what and how to search—pure retrieval without sufficient generative capacity is insufficient.

"You need some baseline intelligence to make all this work."
— Host [23:47]
Tool Use in RL:
Models often fail to persist in using tools after early failures—training models require not only correct tools, but strategies to recover from errors and experiment.

7. Technical Research Frontiers: Tooling, Skill Taxonomy, Planning [35:13, 38:09]

Research Opportunities:
- Multitool RL: Loop, Retool, Toral, etc. explore using multiple tools within RL environments.
- Academic Impact: Transitioning from research papers to artifacts (datasets, evals, agents) for real-world measurement.
- Custom domains (e.g., academic document search): Niche, targeted evals make agent research more tractable for academia.
Skills and Taxonomy:
Nathan introduces four key bottlenecks for future agents:
1. Skills — RL to achieve high evals (already happening, e.g., O&R1).
2. Abstraction — Breaking down tasks into tractable parts.
3. Strategy — Planning steps effectively.
4. Calibration — Avoiding overthinking, knowing when to stop and ask for help.
"Planning is a word that people already use a lot. Strategy would be the direction the model should go in... Abstraction is how does it break it down into things it can actually solve... Calibration is not wasting compute and knowing when to give up."
— Nathan [39:13]

8. Over-Optimization, Reward Hacking, and Industry Learning [54:34]

Over-Optimization in RL:
RL—whether in control, RLHF, or RLVR settings—always risks exploiting weaknesses in reward signals.
Examples:
- In code RL, models learn to pass unit tests by exploiting simple logic (e.g., using "pass" statements).
- Reward design remains a frontier, especially as training spans across different domains (code, math, retrieval).
Quote:
"All these over-optimizations are just the model optimizer is strong enough where it can manipulate the agent... to its target signal..."
— Nathan [55:07]

9. Open Source Models, Model Spec, and the Character/Personality Frontier [62:20+]

Character Training & Model Specification:
- The "model spec" work at OpenAI is highlighted as a breakthrough in declarative model behavioral intent—more actionable (both for developers and regulators) than constitutions.
- Personality, custom agent behaviors, and roleplay are critical but under-indexed areas, especially as open models look for differentiation from closed models.
Quote:
“Model spec is much more useful than a constitution. The constitution is an intermediate training artifact… but with a model spec, you’re signaling what the model is intended to do.”
— Nathan [63:24]

10. Infrastructure, Parallelism, and the Industry Landscape [46:45, 49:56, 69:25]

Parallelism:
Technologies like O1-Pro and DeepThink use parallel generations with reward models to identify best answers, mainly for robustness rather than increased depth.
- Potential for more impact exists if better verifiers (beyond simple reward models) are developed.
On-Device/Local Models:
The future of open models includes hopes for on-device, highly specialized models, but most users still access through APIs due to infra costs.
Industry Dynamics:
Meta’s massive open model releases, hiring wars, and industry “psyops” are dissected.
- Talent is getting more expensive than compute in some ways.
- OpenAI model releases are expected to continually set new benchmarks.

Notable Quotes & Memorable Moments

On Naming RLVR:
"It's also very clear of like RLHF is four letters. It's like we want to evolve that and have a similar four letter acronym. It's not that much magic to it..." — Nathan [05:38]
On Frontier Human Data:
"We still don't have the answered question on how important human is versus AI feedback. Every time I check in with people at Frontier Labs, they're like, yeah, we still use human preference data. And I'm like, okay, I don't have access to that and I don't know how to measure how, how much it gives you." — Nathan [11:32]
On RLHF Book and Field Pace:
"RLVR is going to be changing so much in the next 18 months... We've already seen it. There's all these new algorithms..." — Nathan [15:47]
On Over-Optimization:
"All of these over-optimizations are just the model optimizer is strong enough where it can manipulate the agent with respect to the environment or manipulate the environment in a way that's useful to its target signal." — Nathan [55:07]
On OpenAI and Model Transparency:
"It's real because of what it sends to develop. It has the developer benefit of where your model's going. And then also just regulatory..." — Nathan [63:24]

Important Timestamps

00:53 Congratulations and recap of Nathan’s journey & role
01:33 Transition from TOLU to RLVR: motivations and context
06:19 RLVR technical overview, distinctions from RLHF
09:04 Data bottlenecks and verification as core challenges
12:46 Human preference data, Chatbot Arena, attention economy, and evaluation skepticism
15:47 RLHF book status, field velocity, and why RLVR is not yet mature enough
17:56 Recent advances: O3, Gemini 2.5, Claude; hybrid vs. dedicated reasoning models
22:08 Tool use, agentic environments, and the future of LLM+search
29:56 RL agents learning to persist in tool use; failure cases for agent/tool co-design
35:13 Multitool RL research frontiers, academic niches for impactful research
38:09 Fourfold taxonomy for RL agent skills: skills, abstraction, strategy, calibration
54:34 Reward hacking and over-optimization; history and practical lessons
62:20 Model specs, personality training, and the underappreciated character/personalization axis
69:25 On-device models, infrastructure, and economic realities of local vs. API use
74:05 Meta’s "panic button" and the shifting economics of talent and AI research
75:53 “Building the American DeepSeek” — Nathan outlines his vision for open, American-scale frontier models

Key Takeaways

RLVR is an evolutionary step beyond RLHF, focusing on verifiable, not just human, rewards—enabling reliable wide-domain post-training.
Data—especially verifiable, high-quality, open preference data—is still the fundamental bottleneck for aligning and advancing open models.
Agentic reasoning models using flexible search/tooling architectures are becoming the industry norm at the frontier; academic and open community must find niches and leverage transparency to remain competitive.
Character, personality, and model specs are ripe, underexplored research frontiers for open models—both for differentiation from closed LLMs and for regulatory clarity.
Parallelism and better verifiers may yield new robustness and efficiency, but infrastructure and compute costs remain significant headwinds.
The future of open-source AI hinges on keeping pace not only with open weights, but with open methods, open evals, and reproducible artifact-centric research.

For more details, advanced listeners should check out:

Listen to the full episode and find show notes at Latent Space.

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The RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)

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Summary

Podcast Summary: The RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)

Overview

Key Discussion Points

1. The Journey from TOLU to RLVR [01:33]

2. RLVR vs RLHF and the State of Post-Training [05:38]

3. Data, Preference Modeling, and Verification Bottlenecks [08:32]

4. Chatbot Arena, Attention Economy, and Evaluation [12:46]

5. RLVR, RLHF, and the Next Technical Frontier [15:31]

6. Search, Long Context, and Tool Use in Agents [22:08]

7. Technical Research Frontiers: Tooling, Skill Taxonomy, Planning [35:13, 38:09]

8. Over-Optimization, Reward Hacking, and Industry Learning [54:34]

9. Open Source Models, Model Spec, and the Character/Personality Frontier [62:20+]

10. Infrastructure, Parallelism, and the Industry Landscape [46:45, 49:56, 69:25]

Notable Quotes & Memorable Moments

Important Timestamps

Key Takeaways

For more details, advanced listeners should check out:

Summary

Podcast Summary: The RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)

Overview

Key Discussion Points

1. The Journey from TOLU to RLVR [01:33]

2. RLVR vs RLHF and the State of Post-Training [05:38]

3. Data, Preference Modeling, and Verification Bottlenecks [08:32]

4. Chatbot Arena, Attention Economy, and Evaluation [12:46]

5. RLVR, RLHF, and the Next Technical Frontier [15:31]

6. Search, Long Context, and Tool Use in Agents [22:08]

7. Technical Research Frontiers: Tooling, Skill Taxonomy, Planning [35:13, 38:09]

8. Over-Optimization, Reward Hacking, and Industry Learning [54:34]

9. Open Source Models, Model Spec, and the Character/Personality Frontier [62:20+]

10. Infrastructure, Parallelism, and the Industry Landscape [46:45, 49:56, 69:25]

Notable Quotes & Memorable Moments

Important Timestamps

Key Takeaways

For more details, advanced listeners should check out: